{"version":"0.1","company":{"name":"YubHub","url":"https://yubhub.co","jobsUrl":"https://yubhub.co/jobs/skill/torch"},"x-facet":{"type":"skill","slug":"torch","display":"Torch","count":100},"x-feed-size-limit":100,"x-feed-sort":"enriched_at desc","x-feed-notice":"This feed contains at most 100 jobs (the most recently enriched). For the full corpus, use the paginated /stats/by-facet endpoint or /search.","x-generator":"yubhub-xml-generator","x-rights":"Free to redistribute with attribution: \"Data by YubHub (https://yubhub.co)\"","x-schema":"Each entry in `jobs` follows https://schema.org/JobPosting. YubHub-native raw fields carry `x-` prefix.","jobs":[{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_dd821a22-5a6"},"title":"Abschlussarbeit (J000020339)","description":"<p>The increased use of lithium-ion batteries in future electric vehicles presents new challenges to systems security, particularly in terms of high energy densities and increased performance demands. A particularly safety-relevant scenario is thermal runaway of individual battery cells. In this process, a highly energetic gas-particle stream is released, characterised by strong transient thermal, mechanical, and abrasive loads. These stresses can cause significant damage to adjacent components and lead to structural failure.</p>\n<p>The goal of this work is to develop a predictive method for evaluating material performance under thermal runaway conditions due to escaping gas and particles at materials used in venting structures. To achieve this, a neural network will be designed, trained, and applied to new materials. The neural network will be trained and validated using existing experimental and simulation data. The generated evaluation data will be compared with results from classical substitute and cell tests to evaluate the performance and reliability of the developed approach. Based on the obtained data, characteristic parameters for evaluating material failure under thermal runaway conditions will be identified and derived.</p>\n<p>In the first step, a systematic literature review will be conducted on existing experimental, analytical, and simulation methods for evaluating fire protection materials in the thermal runaway context. Based on this, a suitable model for evaluating material performance in the context of thermal runaway will be developed, trained, and implemented. The model will be validated using experimental data to assess its predictive accuracy and robustness. Finally, the applicability of the developed approach and potential opportunities for further development will be critically discussed.</p>\n<p>Key tasks:</p>\n<ul>\n<li>Conduct a systematic literature review on existing experimental, analytical, and simulation methods for evaluating fire protection materials in the thermal runaway context.</li>\n</ul>\n<ul>\n<li>Design, train, and implement a neural network for predictive evaluation of material performance under thermal runaway conditions.</li>\n</ul>\n<ul>\n<li>Evaluate and analyse experimental and simulation data to validate the model.</li>\n</ul>\n<ul>\n<li>Compare generated evaluation data with results from classical substitute and cell tests.</li>\n</ul>\n<ul>\n<li>Identify and derive characteristic parameters for evaluating material failure under thermal runaway conditions.</li>\n</ul>\n<ul>\n<li>Critically discuss the applicability of the developed approach and potential opportunities for further development.</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>Bachelor&#39;s or master&#39;s degree in computer science, mechanical engineering, electrical engineering, or a related field.</li>\n</ul>\n<ul>\n<li>Experience in machine learning and deep learning.</li>\n</ul>\n<ul>\n<li>Familiarity with Python programming language and relevant libraries.</li>\n</ul>\n<ul>\n<li>Good understanding of thermal runaway phenomena and fire protection materials.</li>\n</ul>\n<ul>\n<li>Excellent communication and teamwork skills.</li>\n</ul>\n<ul>\n<li>Ability to work independently and manage multiple tasks.</li>\n</ul>\n<p>Preferred skills:</p>\n<ul>\n<li>Experience with neural networks and deep learning frameworks such as TensorFlow or PyTorch.</li>\n</ul>\n<ul>\n<li>Familiarity with simulation software such as ANSYS or COMSOL.</li>\n</ul>\n<ul>\n<li>Knowledge of thermal analysis and heat transfer.</li>\n</ul>\n<ul>\n<li>Experience with data analysis and visualisation tools such as Matplotlib or Seaborn.</li>\n</ul>\n<ul>\n<li>Familiarity with version control systems such as Git.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_dd821a22-5a6","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Dr. Ing. h.c. 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As a Research Scientist, you will be responsible for researching and developing new methods for simulating sensor data, as well as collaborating with other teams to integrate these simulations into our production processes.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Research and develop new methods for simulating sensor data</li>\n<li>Collaborate with other teams to integrate sensor simulations into production processes</li>\n<li>Analyze and optimize sensor simulation algorithms for improved accuracy and efficiency</li>\n<li>Develop and maintain documentation for sensor simulation methods and tools</li>\n<li>Present research findings and results to internal stakeholders</li>\n</ul>\n<p><strong>Requirements</strong></p>\n<ul>\n<li>PhD in Computer Science, Mathematics, or related field</li>\n<li>Strong background in machine learning and deep learning</li>\n<li>Experience with computer vision and image processing</li>\n<li>Proficiency in Python and C++ programming languages</li>\n<li>Excellent communication and collaboration skills</li>\n</ul>\n<p><strong>Nice to Have</strong></p>\n<ul>\n<li>Experience with Gaussian splatting and other sensor simulation techniques</li>\n<li>Familiarity with popular machine learning frameworks such as TensorFlow and PyTorch</li>\n<li>Experience with Linux operating system and Git version control</li>\n</ul>\n<p><strong>Benefits</strong></p>\n<ul>\n<li>Competitive salary and benefits package</li>\n<li>Opportunity to work with a leading automotive manufacturer</li>\n<li>Collaborative and dynamic work environment</li>\n<li>Professional development opportunities</li>\n</ul>\n<p>If you&#39;re passionate about research and development, and want to contribute to the creation of innovative sensor simulation techniques, we encourage you to apply for this exciting opportunity!</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_468176dd-4f0","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Porsche","sameAs":"https://jobs.porsche.com","logo":"https://logos.yubhub.co/jobs.porsche.com.png"},"x-apply-url":"https://jobs.porsche.com/index.php?ac=jobad&id=20313","x-work-arrangement":"onsite","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Machine Learning","Deep Learning","Computer Vision","Image Processing","Python","C++"],"x-skills-preferred":["Gaussian Splatting","TensorFlow","PyTorch","Linux","Git"],"datePosted":"2026-04-22T17:28:18.205Z","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Automotive","skills":"Machine Learning, Deep Learning, Computer Vision, Image Processing, Python, C++, Gaussian Splatting, TensorFlow, PyTorch, Linux, Git"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_121112f5-aa8"},"title":"Practice Lead Data Science & AI Engineering","description":"<p>Are you interested in shaping the future of data-driven and intelligent systems - from classic AI use cases to physical AI in real-world applications (e.g., production, simulation, robotics, autonomous systems)? Then take responsibility for building and scaling our Practice.</p>\n<p>Your tasks will include:</p>\n<ul>\n<li>Leading and developing the Practice Data Science &amp; AI Engineering - technically and disciplinarily - and scaling teams, structures, and competences</li>\n<li>Being responsible for the strategic further development of our AI portfolio - especially in the areas of machine learning, computer vision, physical AI, and quantum computing</li>\n<li>Defining technical standards and reference architectures for AI, vision, and edge systems</li>\n<li>Identifying new use cases (e.g., visual quality inspection, autonomous systems, intelligent sensors, generative design) and developing scalable solutions for our customers</li>\n<li>Driving business development forward, developing offer portfolios, and supporting presales through technical solution designs</li>\n<li>Being responsible for expanding your team and managing the economic steering of the practice (e.g., utilization, growth, profitability)</li>\n</ul>\n<p>To be well-prepared for your path, you should have the following qualifications:</p>\n<ul>\n<li>Completed studies and several years of experience in building and leading technical teams or practices</li>\n<li>Passion for implementing complex AI or vision projects and technical solution design and presales</li>\n<li>Expertise in relevant technologies such as Python, TensorFlow, PyTorch, OpenCV, Kubernetes, and cloud or edge platforms (AWS, Azure, GCP)</li>\n<li>Knowledge in machine learning, AI systems, and computer vision and physical AI</li>\n<li>Your work style is characterized by analytical thinking, leadership, pragmatism, and strong team and customer orientation</li>\n</ul>\n<p>Important information before departure:</p>\n<ul>\n<li>Start date: after agreement - always at the beginning of a month</li>\n<li>Working hours: full-time (40 hours) and/or part-time possible; 30 vacation days</li>\n<li>Employment relationship: Unlimited</li>\n<li>Field: Consulting</li>\n<li>Language: Secure German and English</li>\n<li>Flexibility and travel readiness</li>\n<li>Other: Valid work permit; if necessary, we can apply for a work permit within our recruitment process. The procedure takes time and affects the start date</li>\n</ul>\n<p>As a technology and business partner, MHP digitalizes processes and products for its customers and accompanies them in their IT transformations along the entire value chain. With over 25 years of experience, MHP has grown to become a leading player in the industry, with a global presence and a team of over 3,000 experts. MHP&#39;s mission is to drive excellence and sustainable success, and the company is committed to delivering high-quality services and solutions to its customers.</p>\n<p>At MHP, you will have the opportunity to grow and develop your skills in a dynamic and supportive environment. We offer a range of benefits, including flexible working hours, a comprehensive training program, and opportunities for professional development. Our team is passionate about innovation and customer satisfaction, and we are committed to making a positive impact on our customers&#39; businesses.</p>\n<p>If you are interested in this opportunity, please submit your application through our job locator. We look forward to hearing from you!</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_121112f5-aa8","directApply":true,"hiringOrganization":{"@type":"Organization","name":"MHP","sameAs":"https://jobs.porsche.com","logo":"https://logos.yubhub.co/jobs.porsche.com.png"},"x-apply-url":"https://jobs.porsche.com/index.php?ac=jobad&id=20141","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"Competitive salary and benefits package","x-skills-required":["Python","TensorFlow","PyTorch","OpenCV","Kubernetes","Cloud or edge platforms (AWS, Azure, GCP)","Machine learning","AI systems","Computer vision","Physical AI"],"x-skills-preferred":[],"datePosted":"2026-04-22T17:26:47.887Z","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, TensorFlow, PyTorch, OpenCV, Kubernetes, Cloud or edge platforms (AWS, Azure, GCP), Machine learning, AI systems, Computer vision, Physical AI"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_1bd2d1b2-84f"},"title":"Senior Machine Learning Researcher","description":"<p>We are seeking a senior machine learning researcher to join our Core AI team.</p>\n<p>As part of the team, you will help solve complex business problems by developing viable cutting-edge AI/ML solutions.</p>\n<p>You will develop and implement creative solutions that fundamentally transform business processes, delivering breakthrough improvements rather than incremental changes.</p>\n<p>You will work closely with other AI/ML researchers and engineers, SWEs, product owners/managers, and business stakeholders, and participate in the full lifecycle of solution development, including requirements gathering with business, experimentation and algorithmic exploration, development, and assistance with productization.</p>\n<p>Key Responsibilities:</p>\n<ul>\n<li>Work independently or as part of a team to help design and implement high accuracy and delightful user experience solutions utilizing ML, NLP, GenAI, Agentic technologies.</li>\n</ul>\n<ul>\n<li>Participate in all aspects of solution development, including ideation and requirement gathering with business stakeholders, experimentation and exploration to identify strong solution approaches, solution development, etc.</li>\n</ul>\n<ul>\n<li>Prototype, test, and iterate on novel AI models and approaches to solve complex business challenges.</li>\n</ul>\n<ul>\n<li>Collaborate with cross-functional teams to identify opportunities where AI can create significant business value, and transition solutions into production systems.</li>\n</ul>\n<ul>\n<li>Research and stay updated with the latest advancements in machine learning and AI technologies.</li>\n</ul>\n<ul>\n<li>Participate in code reviews, technical discussions, and knowledge sharing sessions.</li>\n</ul>\n<ul>\n<li>Communicate technical concepts and transformative ideas effectively to both technical and non-technical stakeholders.</li>\n</ul>\n<p>Required Skills &amp; Qualifications:</p>\n<ul>\n<li>Bachelor&#39;s with 10+ years, Master&#39;s with 7+ years, or PhD with 5+ years in Computer Science, Data Science, Machine Learning, or related field.</li>\n</ul>\n<ul>\n<li>Deep expertise and proven ability in developing high accuracy/value solutions to business problems in the NLP, Generative AI, Agentic AI, and/or ML space.</li>\n</ul>\n<ul>\n<li>Hands-on experience with data processing, experimentation, and exploration.</li>\n</ul>\n<ul>\n<li>Strong programming skills in Python.</li>\n</ul>\n<ul>\n<li>Experience with cloud platforms (AWS, Azure, GCP) for deploying ML solutions.</li>\n</ul>\n<ul>\n<li>Excellent problem-solving skills and attention to detail.</li>\n</ul>\n<ul>\n<li>Strong communication skills to collaborate with technical and non-technical stakeholders.</li>\n</ul>\n<ul>\n<li>Ability to work independently and collaboratively.</li>\n</ul>\n<p>Additional Preferred Skills &amp; Qualifications:</p>\n<ul>\n<li>Understanding of the financial markets, including experience with financial datasets, is strongly preferred.</li>\n</ul>\n<ul>\n<li>Experience with ML frameworks such as PyTorch, TensorFlow.</li>\n</ul>\n<ul>\n<li>Familiarity with MLOps practices and tools such as SageMaker, MLflow, or Airflow.</li>\n</ul>\n<ul>\n<li>Previous experience working in an Agile environment.</li>\n</ul>\n<p>Millennium pays a total compensation package which includes a base salary, discretionary performance bonus, and a comprehensive benefits package. The estimated base salary range for this position is $175,000 to $250,000, which is specific to New York and may change in the future.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_1bd2d1b2-84f","directApply":true,"hiringOrganization":{"@type":"Organization","name":"IT - Artificial Intelligence","sameAs":"https://mlp.eightfold.ai","logo":"https://logos.yubhub.co/mlp.eightfold.ai.png"},"x-apply-url":"https://mlp.eightfold.ai/careers/job/755954012324","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$175,000 to $250,000","x-skills-required":["Python","Machine Learning","NLP","GenAI","Agentic technologies","Data processing","Experimentation","Exploration","Cloud platforms (AWS, Azure, GCP)","Problem-solving skills","Communication skills"],"x-skills-preferred":["PyTorch","TensorFlow","MLOps practices and tools (SageMaker, MLflow, Airflow)","Agile environment"],"datePosted":"2026-04-18T22:14:27.951Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"New York, New York, United States of America"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Machine Learning, NLP, GenAI, Agentic technologies, Data processing, Experimentation, Exploration, Cloud platforms (AWS, Azure, GCP), Problem-solving skills, Communication skills, PyTorch, TensorFlow, MLOps practices and tools (SageMaker, MLflow, Airflow), Agile environment","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":175000,"maxValue":250000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_0987988a-011"},"title":"Feature Framework Engineer","description":"<p>The Systematic Platform Execution &amp; Exchange Data (SPEED) Team is at the core of Millennium&#39;s Equities, Quant Strategies, and Shared Services Technology organisation.</p>\n<p>We are looking for a C++ engineer to design and build high-performance, low-latency applications that process large volumes of real-time data.</p>\n<p>Principal Responsibilities:</p>\n<ul>\n<li>Design, implement, and maintain high-performance C++ services handling high message rates and low-latency workloads.</li>\n</ul>\n<ul>\n<li>Optimise existing components for latency, throughput, and CPU/memory efficiency.</li>\n</ul>\n<ul>\n<li>Develop and tune networking, messaging, and I/O layers to handle large data volumes reliably.</li>\n</ul>\n<ul>\n<li>Profile and debug performance issues at application, OS, and network levels.</li>\n</ul>\n<ul>\n<li>Collaborate with quantitative, trading, and infrastructure teams to translate requirements into robust technical solutions.</li>\n</ul>\n<ul>\n<li>Write clean, production-quality code with appropriate tests and 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environment.</li>\n</ul>\n<ul>\n<li>Clear communicator who can work closely with both technical and non-technical stakeholders.</li>\n</ul>\n<ul>\n<li>Proactive, self-directed, and able to thrive in a highly iterative environment.</li>\n</ul>\n<p>The estimated base salary range for this position is $175,000 to $250,000, which is specific to New York and may change in the future.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_0987988a-011","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Unknown","sameAs":"https://mlp.eightfold.ai","logo":"https://logos.yubhub.co/mlp.eightfold.ai.png"},"x-apply-url":"https://mlp.eightfold.ai/careers/job/755955682418","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$175,000 to $250,000","x-skills-required":["modern C++","KDB / 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As a key member of our GPS Engineering team, you will lead the charge in research into Agent design, Deep Research and AI Safety/reliability, developing novel methodologies that not only power public sector applications but set new standards across the entire Scale organisation.</p>\n<p>Your mission is threefold:</p>\n<ul>\n<li>Frontier Research &amp; Publication: Leading research into LLM/agent capabilities, reasoning, and safety, with the goal of publishing at top-tier venues (NeurIPS, ICML, ICLR).</li>\n<li>Cross-Org Impact: Developing generalised techniques in Agent design, AI Safety and Deep Research agents that scale across our commercial and government platforms.</li>\n<li>Mission-Critical Applications: Engineering high-stakes AI systems that impact millions of citizens globally.</li>\n</ul>\n<p>You will:</p>\n<ul>\n<li>Pioneer Novel Architectures: Design and train state-of-the-art models and agents, moving beyond “off-the-shelf” solutions to create custom architectures for complex public sector reasoning tasks.</li>\n<li>Lead AI Safety Initiatives: Research and implement robust safety frameworks, including red teaming, alignment (RLHF/DPO), and bias mitigation strategies essential for sovereign AI.</li>\n<li>Drive Deep Research Capabilities: Develop agents capable of long-horizon reasoning and autonomous information synthesis to solve complex problems for national security and public policy.</li>\n<li>Publish and Contribute: Represent Scale in the broader research community by publishing high-impact papers and contributing to open-source breakthroughs.</li>\n<li>Consult as a Subject Matter Expert: Act as a technical authority for public sector leaders, advising on the theoretical limits and safety requirements of emerging AI.</li>\n<li>Build Evaluation Frontiers: Create new benchmarks and evaluation protocols that define what success looks like for high-stakes, non-commercial AI applications.</li>\n</ul>\n<p>Ideally, you’d have:</p>\n<ul>\n<li>Advanced Degree: PhD or Master’s in Computer Science, Mathematics, or a related field with a focus on Deep Learning.</li>\n<li>Research Track Record: A portfolio of first-author publications at major conferences (NeurIPS, ICML, CVPR, EMNLP, etc.).</li>\n<li>Engineering Rigour: Strong proficiency in Python, deep learning frameworks (PyTorch/JAX), with the ability to write production-ready code that scales.</li>\n<li>Safety Expertise: Experience in alignment, robustness, or interpretability research.</li>\n</ul>\n<p>Nice to haves:</p>\n<ul>\n<li>Experience with large-scale distributed training on massive clusters.</li>\n<li>Experience in building agentic systems that are reliable.</li>\n<li>Experience in Sovereign AI or working with highly regulated data environments.</li>\n<li>A zero-to-one mindset: Comfortable navigating ambiguity and defining research directions from scratch.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_1c4de3ab-a58","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Scale","sameAs":"https://scale.com/","logo":"https://logos.yubhub.co/scale.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/scaleai/jobs/4413274005","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Python","Deep Learning","PyTorch","JAX","AI Safety","Alignment","Robustness","Interpretability"],"x-skills-preferred":["Large-scale Distributed Training","Agentic Systems","Sovereign AI","Regulated Data Environments"],"datePosted":"2026-04-18T15:59:21.005Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Doha, Qatar; London, UK"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Deep Learning, PyTorch, JAX, AI Safety, Alignment, Robustness, Interpretability, Large-scale Distributed Training, Agentic Systems, Sovereign AI, Regulated Data Environments"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_5aa5b947-f4d"},"title":"Staff Machine Learning Research Scientist/ Engineer, Agents","description":"<p>About Scale AI</p>\n<p>At Scale AI, our mission is to accelerate the development of AI applications. This role is at the intersection of cutting-edge AI research and practical application, with a focus on studying the data types essential for building state-of-the-art agents.</p>\n<p>Responsibilities</p>\n<ul>\n<li>Explore the data landscape needed to advance intelligent, adaptable AI agents, guiding the data strategy at Scale to drive innovation.</li>\n<li>Contribute to impactful research publications on agents, collaborate with customer researchers, and work alongside the engineering team to translate these advancements into real-world, scalable solutions.</li>\n</ul>\n<p>Requirements</p>\n<ul>\n<li>Practical experience working with LLMs, with proficiency in frameworks like Pytorch, Jax, or Tensorflow.</li>\n<li>A track record of published research in top ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, COLM, etc.).</li>\n<li>At least three years of experience addressing sophisticated ML problems, either in a research setting or product development.</li>\n</ul>\n<p>Nice to Have</p>\n<ul>\n<li>Hands-on experience with open source LLM fine-tuning or involvement in bespoke LLM fine-tuning projects using Pytorch/Jax.</li>\n<li>Hands-on experience and publications in building applications and evaluations related to AI agents such as tool-use, text2SQL, browser agents, coding agents and GUI agents.</li>\n<li>Hands-on experience with agent frameworks such as OpenHands, Swarm, LangGraph, etc.</li>\n<li>Familiarity with agentic reasoning methods such as STaR and PLANSEARCH</li>\n<li>Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment.</li>\n</ul>\n<p>Benefits</p>\n<ul>\n<li>Comprehensive health, dental and vision coverage</li>\n<li>Retirement benefits</li>\n<li>A learning and development stipend</li>\n<li>Generous PTO</li>\n<li>Commuter stipend</li>\n</ul>\n<p>Salary Range</p>\n<p>$259,200-$324,000 USD</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_5aa5b947-f4d","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Scale AI","sameAs":"https://scale.com/","logo":"https://logos.yubhub.co/scale.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/scaleai/jobs/4488520005","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$259,200-$324,000 USD","x-skills-required":["Pytorch","Jax","Tensorflow","LLMs","Agent frameworks","Agentic reasoning methods","Cloud technology stack"],"x-skills-preferred":["Open source LLM fine-tuning","Bespoke LLM fine-tuning projects"],"datePosted":"2026-04-18T15:59:17.656Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA; Seattle, WA; New York, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Pytorch, Jax, Tensorflow, LLMs, Agent frameworks, Agentic reasoning methods, Cloud technology stack, Open source LLM fine-tuning, Bespoke LLM fine-tuning projects","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":259200,"maxValue":324000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_fb1f459e-b3a"},"title":"Machine Learning Research Scientist / Engineer, Reasoning","description":"<p>About Scale</p>\n<p>At Scale, our mission is to accelerate the development of AI applications. We&#39;re looking for a Machine Learning Research Scientist/Engineer to join our team and help us shape the future of AI.</p>\n<p>This role operates at the forefront of AI research and real-world implementation, with a strong focus on reasoning within large language models (LLMs). You will study the data types critical for advancing LLM-based agents, including browser and software engineering (SWE) agents. You will play a key role in shaping Scale&#39;s data strategy by identifying the most effective data sources and methodologies for improving LLM reasoning.</p>\n<p>Success in this role requires a deep understanding of LLMs, planning algorithms, and novel approaches to agentic reasoning, as well as creativity in tackling challenges related to data generation, model interaction, and evaluation. You will contribute to impactful research on language model reasoning, collaborate with external researchers, and work closely with engineering teams to bring state-of-the-art advancements into scalable, real-world solutions.</p>\n<p>Responsibilities</p>\n<ul>\n<li>Study the data types critical for advancing LLM-based agents, including browser and software engineering (SWE) agents</li>\n<li>Shape Scale&#39;s data strategy by identifying the most effective data sources and methodologies for improving LLM reasoning</li>\n<li>Contribute to impactful research on language model reasoning</li>\n<li>Collaborate with external researchers</li>\n<li>Work closely with engineering teams to bring state-of-the-art advancements into scalable, real-world solutions</li>\n</ul>\n<p>Requirements</p>\n<ul>\n<li>Practical experience working with LLMs, with proficiency in frameworks like PyTorch, JAX, or TensorFlow</li>\n<li>A track record of published research in top ML and NLP venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, CoLLM, etc.)</li>\n<li>At least three years of experience solving complex ML challenges, either in a research setting or product development, particularly in areas related to LLM capabilities and reasoning</li>\n<li>Strong written and verbal communication skills, along with the ability to work effectively across teams</li>\n</ul>\n<p>Nice to Have</p>\n<ul>\n<li>Hands-on experience fine-tuning open-source LLMs or leading bespoke LLM fine-tuning projects using PyTorch/JAX</li>\n<li>Research and practical experience in building applications and evaluations related to LLM-based agents, including tool-use, text-to-SQL, browser agents, coding agents, and GUI agents</li>\n<li>Experience with agent frameworks such as OpenHands, Swarm, LangGraph, or similar</li>\n<li>Familiarity with advanced agentic reasoning techniques such as STaR and PLANSEARCH</li>\n<li>Proficiency in cloud-based ML development, with experience in AWS or GCP environments</li>\n</ul>\n<p>Benefits</p>\n<ul>\n<li>Comprehensive health, dental and vision coverage</li>\n<li>Retirement benefits</li>\n<li>A learning and development stipend</li>\n<li>Generous PTO</li>\n<li>Commuter stipend</li>\n</ul>\n<p>Salary Range</p>\n<p>$252,000-$315,000 USD</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_fb1f459e-b3a","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Scale AI","sameAs":"https://scale.com/","logo":"https://logos.yubhub.co/scale.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/scaleai/jobs/4605596005","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$252,000-$315,000 USD","x-skills-required":["PyTorch","JAX","TensorFlow","Large Language Models (LLMs)","Planning Algorithms","Agentic Reasoning","Data Generation","Model Interaction","Evaluation"],"x-skills-preferred":["Agent Frameworks","Cloud-Based ML Development","AWS","GCP","STaR","PLANSEARCH"],"datePosted":"2026-04-18T15:59:07.207Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA; Seattle, WA; New York, NY"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"PyTorch, JAX, TensorFlow, Large Language Models (LLMs), Planning Algorithms, Agentic Reasoning, Data Generation, Model Interaction, Evaluation, Agent Frameworks, Cloud-Based ML Development, AWS, GCP, STaR, PLANSEARCH","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":252000,"maxValue":315000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_840bab06-7be"},"title":"ML Research Engineer, ML Systems","description":"<p>Job Description:</p>\n<p>Scale&#39;s ML platform (RLXF) team builds our internal distributed framework for large language model training and inference. The platform has been powering MLEs, researchers, data scientists and operators for fast and automatic training and evaluation of LLM&#39;s, as well as evaluation of data quality.</p>\n<p>At Scale, we&#39;re uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle. You will work closely across Scale&#39;s ML teams and researchers to build the foundation platform that supports all our ML research and development. You will be building and optimizing the platform to enable our next generation of LLM training, inference and data curation.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Build, profile and optimize our training and inference framework</li>\n<li>Collaborate with ML teams to accelerate their research and development and enable them to develop the next generation of models and data curation</li>\n<li>Research and integrate state-of-the-art technologies to optimize our ML system</li>\n</ul>\n<p>Ideal Candidate:</p>\n<ul>\n<li>Strong excitement about system optimization</li>\n<li>Experience with multi-node LLM training and inference</li>\n<li>Experience with developing large-scale distributed ML systems</li>\n<li>Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc.</li>\n<li>Strong written and verbal communication skills and the ability to operate in a cross functional team environment</li>\n</ul>\n<p>Nice to Have:</p>\n<ul>\n<li>Demonstrated expertise in post-training methods &amp;/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc.</li>\n</ul>\n<p>Compensation Packages:</p>\n<p>Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You&#39;ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.</p>\n<p>Please note that our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_840bab06-7be","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Scale","sameAs":"https://scale.com/","logo":"https://logos.yubhub.co/scale.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/scaleai/jobs/4534631005","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$189,600-$237,000 USD","x-skills-required":["System Optimization","Multi-node LLM Training and Inference","Large-Scale Distributed ML Systems","CUDA","Pytorch","Transformers","Flash Attention"],"x-skills-preferred":["Post-Training Methods","Next Generation Use Cases for Large Language Models","Instruction Tuning","RLHF","Tool Use","Reasoning","Agents","Multimodal"],"datePosted":"2026-04-18T15:58:47.020Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA; Seattle, WA; New York, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"System Optimization, Multi-node LLM Training and Inference, Large-Scale Distributed ML Systems, CUDA, Pytorch, Transformers, Flash Attention, Post-Training Methods, Next Generation Use Cases for Large Language Models, Instruction Tuning, RLHF, Tool Use, Reasoning, Agents, Multimodal","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":189600,"maxValue":237000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_8a3caae4-044"},"title":"Member of Technical Staff - Imagine Model","description":"<p>As a Member of Technical Staff on the Imagine Model Team, you will develop cutting-edge AI experiences beyond text, with a strong focus on enabling high-fidelity understanding and generation across image and video modalities. Responsibilities span data curation, modeling, training, inference serving, and product integration, covering both pretraining and post-training phases. You will collaborate closely with product teams to push model frontiers and deliver exceptional end-to-end user experiences.</p>\n<p>Key responsibilities include creating and driving engineering agendas to advance multimodal capabilities, improving data quality through annotation, filtering, augmentation, synthetic generation, captioning, and in-depth data studies, designing evaluation frameworks, metrics, benchmarks, evals, and reward models tailored to image/video/audio quality and coherence, implementing efficient algorithms for state-of-the-art model performance, and developing scalable data collection and processing pipelines for multimodal (primarily image/video-focused) datasets.</p>\n<p>The ideal candidate will have a track record in leading studies that significantly improve neural network capabilities and performance through better data or modeling, experience in data-driven experiment designs, systematic analysis, and iterative model debugging, experience developing or working with large-scale distributed machine learning systems, and ability to deliver optimal end-to-end user experiences.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_8a3caae4-044","directApply":true,"hiringOrganization":{"@type":"Organization","name":"xAI","sameAs":"https://www.xai.com/","logo":"https://logos.yubhub.co/xai.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/xai/jobs/5051985007","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$180,000 - $440,000 USD","x-skills-required":["data curation","modeling","training","inference serving","product integration","large-scale distributed machine learning systems"],"x-skills-preferred":["SFT","RL","evals","human/synthetic data collection","agentic systems","Python","JAX/XLA","PyTorch","Rust/C++","Spark","Ray"],"datePosted":"2026-04-18T15:58:43.641Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Palo Alto, CA; Seattle, WA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"data curation, modeling, training, inference serving, product integration, large-scale distributed machine learning systems, SFT, RL, evals, human/synthetic data collection, agentic systems, Python, JAX/XLA, PyTorch, Rust/C++, Spark, Ray","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":180000,"maxValue":440000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_4f808d6c-a4e"},"title":"Machine Learning Research Engineer, GenAI Applied ML","description":"<p><strong>About This Role</strong></p>\n<p>Lead applied ML engineering on Scale&#39;s Applied ML team, powering data infrastructure for leading agentic LLMs (ChatGPT, Gemini, Llama). You will build scalable multi-agent systems to validate agentic reasoning and behaviours, scale human expertise, and drive research into real-world agent reliability failures despite strong benchmarks, shipping production fixes.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Build and deploy multi-agent systems for agentic reasoning validation</li>\n<li>Develop pipelines to detect errors and scale human judgment</li>\n<li>Combine classical ML, LLMs, and multi-agent techniques for reliability</li>\n<li>Lead research into agent failure modes and ship fixes</li>\n<li>Use AI tools to speed prototyping and iteration</li>\n<li>Build data-driven evaluations and deploy rapid improvements</li>\n<li>Integrate systems into Scale&#39;s platform</li>\n</ul>\n<p><strong>Ideal Candidate</strong></p>\n<ul>\n<li>PhD or MSc in Computer Science, Mathematics, Statistics, or related field</li>\n<li>3+ years shipping scaled production ML systems</li>\n<li>Demonstrated real-world impact</li>\n<li>Mastery of PyTorch, TensorFlow, JAX, or scikit-learn</li>\n<li>Deep expertise in agentic LLMs and multi-agent systems</li>\n<li>Strong software engineering and microservices (AWS/GCP)</li>\n<li>Rapid, data-driven iteration</li>\n<li>Proficiency using AI tools to accelerate work</li>\n<li>Strong research depth with practical bias</li>\n<li>Excellent cross-functional communication</li>\n</ul>\n<p><strong>Nice to Have</strong></p>\n<ul>\n<li>Experience prototyping agent evaluation/reliability systems</li>\n<li>Human-in-the-loop or annotation pipeline work</li>\n<li>Open-source contributions in agents, evaluation, or alignment</li>\n<li>Publications on agent reliability (NeurIPS, ICML, ICLR)</li>\n</ul>\n<p><strong>Compensation</strong></p>\n<p>Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity-based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You&#39;ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.</p>\n<p><strong>About Us</strong></p>\n<p>At Scale, our mission is to develop reliable AI systems for the world&#39;s most important decisions. Our products provide the high-quality data and full-stack technologies that power the world&#39;s leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. 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In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave.</p>\n<p>To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. 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Human Frontier Collective (US)","description":"<p>This is a fully remote, 1099 independent contractor opportunity with an estimated duration of six months and the potential for extension.</p>\n<p>As an HFC Fellow, you&#39;ll apply your academic and professional expertise to help design, evaluate, and interpret advanced generative AI systems,while gaining exposure to cutting-edge research and working alongside an interdisciplinary network of leading thinkers.</p>\n<p>You&#39;ll get invited to engage in high-impact projects with our partnered AI labs and platforms, helping models understand real-world deep learning workflows by designing, reviewing, and optimising PyTorch models, evaluating complex ML code and AI-generated implementations for efficiency and correctness, and advising on GPU optimisation, scaling, and trade-offs.</p>\n<p>Beyond the work, you&#39;ll become part of a supportive, interdisciplinary network of innovators and thought leaders committed to advancing frontier AI across domains.</p>\n<p>You&#39;ll also contribute to research publications, collaborating with Scale&#39;s research team to co-author technical reports and research papers,boosting your academic visibility and professional recognition.</p>\n<p>We&#39;re looking for individuals with a PhD or postdoctoral degree in Computer Science, Computer Engineering, or a related field, with 1-3+ years of experience as a Machine Learning Engineer or Data Scientist.</p>\n<p>Key skills include strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow), experience with cloud infrastructure (AWS) and MLOps tools (Docker, Langchain), and a detail-oriented, innovative mindset with a passion in applied AI research and a commitment to collaboration.</p>\n<p>Benefits include professional development, joining a top-tier network, flexible scheduling, and competitive pay.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_4119a38f-6e7","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Human Frontier Collective","sameAs":"https://humanfrontiercollective.com/","logo":"https://logos.yubhub.co/humanfrontiercollective.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/scaleai/jobs/4660340005","x-work-arrangement":"remote","x-experience-level":"mid","x-job-type":"contract","x-salary-range":null,"x-skills-required":["Python","PyTorch","TensorFlow","AWS","Docker","Langchain"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:55:56.161Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"CONTRACTOR","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, PyTorch, TensorFlow, AWS, Docker, Langchain"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_0e93287d-e38"},"title":"Applied Research Engineer","description":"<p>Shape the Future of AI</p>\n<p>At Labelbox, we&#39;re building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we&#39;ve been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially.</p>\n<p>As an Applied Research Engineer at Labelbox, you will be at the forefront of developing cutting-edge systems and methods to create, analyze, and leverage high-quality human-in-the-loop data for frontier model developers. 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We believe that high-quality human data and sophisticated human feedback integration methods are key to unlocking the next generation of AI capabilities. Our research team works at the intersection of machine learning, human-computer interaction, and AI ethics to develop innovative solutions that can be practically applied in real-world scenarios.</p>\n<p>We foster an environment of intellectual curiosity, collaboration, and innovation. We encourage our researchers to explore new ideas, engage in open discussions, and contribute to the wider AI community through publications and conference presentations. 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You&#39;ll prototype new kernel implementations, profile performance across hardware generations, and help define the numerical and parallelism strategies that determine how we scale next-generation AI systems.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Design and implement custom ML kernels (e.g., CUDA, CuTe, Triton) for core LLM operations such as attention, matrix multiplication, gating, and normalization, optimized for modern GPU and accelerator architectures.</li>\n<li>Design and think through compute primitives to reduce memory bandwidth bottlenecks and improve kernel compute efficiency.</li>\n<li>Collaborate with research teams to align kernel-level optimizations with model architecture and algorithmic goals.</li>\n<li>Develop and maintain a library of reusable kernels and performance benchmarks that serve as the foundation for internal model training.</li>\n<li>Contribute to infrastructure stability and scalability, ensuring reproducibility, consistency across precision formats, and high utilization of compute resources.</li>\n<li>Document and share insights through internal talks, technical papers, or open-source contributions to strengthen the broader ML systems community.</li>\n</ul>\n<p><strong>Skills and Qualifications</strong></p>\n<p>Minimum qualifications:</p>\n<ul>\n<li>Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar.</li>\n<li>Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases</li>\n<li>Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.</li>\n<li>Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.</li>\n<li>A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.</li>\n<li>Proficiency in CUDA, CuTe, Triton, or other GPU programming frameworks.</li>\n<li>Demonstrated ability to analyze, profile, and optimize compute-intensive workloads.</li>\n</ul>\n<p>Preferred qualifications:</p>\n<ul>\n<li>Experience training or supporting large-scale language models with tens of billions of parameters or more.</li>\n<li>Track record of improving research productivity through infrastructure design or process improvements.</li>\n<li>Experience developing or tuning kernels for deep learning frameworks such as PyTorch, JAX, or custom accelerators.</li>\n<li>Familiarity with tensor parallelism, pipeline parallelism, or distributed data processing frameworks.</li>\n<li>Experience implementing low-precision formats (FP8, INT8, block floating point) or contributing to related compiler stacks (e.g., XLA, TVM).</li>\n<li>Contributions to open-source GPU, ML systems, or compiler optimization projects.</li>\n<li>Prior research or engineering experience in numerical optimization, communication-efficient training, or scalable AI infrastructure.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_cba88898-896","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Thinking Machines Lab","sameAs":"https://thinkingmachines.ai/","logo":"https://logos.yubhub.co/thinkingmachines.ai.png"},"x-apply-url":"https://job-boards.greenhouse.io/thinkingmachines/jobs/5013934008","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000 - 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As a Machine Learning Intern, you will work on tackling new challenges in machine learning and artificial intelligence. You will join our engineering teams as we maneuver through exponential growth and massive scale while building awesome products and features, creating visually rich experiences, spearheading the discovery problem, and pinpointing tomorrow&#39;s engineering challenges.</p>\n<p>Responsibilities</p>\n<ul>\n<li>Lead your own project start to finish to contribute in cutting-edge research in machine learning and artificial intelligence that can be applied to Pinterest problems</li>\n<li>Collect, analyze, and synthesize findings from data and build intelligent data-driven models</li>\n<li>Write clean, efficient, and sustainable code</li>\n<li>Use machine learning, natural language processing, and graph analysis to solve modeling and ranking problems across discovery, ads and search</li>\n<li>Scope and independently solve moderately complex problems</li>\n<li>Demonstrate accountability for the quality and completion of your tasks and projects, collaborating with your team and seeking guidance as needed</li>\n</ul>\n<p>Requirements</p>\n<ul>\n<li>Working towards a Master&#39;s or PhD degree in Computer Science, ML, NLP, Statistics, Information Sciences or related field</li>\n<li>Machine Learning (ranking, computer vision, NLP, content recommendations, embedding, information retrieval etc)</li>\n<li>Experience with big data technologies (e.g., Hadoop/Spark) and scalable realtime systems that process stream data</li>\n<li>Strong interest in research and applying machine learning and AI to drive meaningful product innovation and user impact</li>\n<li>Exposure to ML, AI, data analytics, statistics, or related technical fields, through research, coursework, projects, or internships</li>\n<li>Proficiency in at least one systems language (Java, C++, Python) or one ML framework (Tensorflow, Pytorch, MLFlow)</li>\n<li>Experience in research and in solving analytical problems</li>\n<li>Strong communicator and team player with the ability to find solutions for open-ended problems</li>\n</ul>\n<p>Why Intern at Pinterest?</p>\n<ul>\n<li>Meaningful Work: Contribute to projects that impact millions of users worldwide.</li>\n<li>Mentorship: Learn from and be guided by experienced engineers and researchers in the field.</li>\n<li>Growth and Development: Participate in professional development workshops and networking events to build your skills and connections.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_1d94b9cf-773","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Pinterest","sameAs":"https://www.pinterest.com/","logo":"https://logos.yubhub.co/pinterest.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/pinterest/jobs/7268778","x-work-arrangement":"hybrid","x-experience-level":"entry","x-job-type":"internship","x-salary-range":"$6,000 - $9,500 CAD monthly","x-skills-required":["Machine Learning","Artificial Intelligence","Python","Java","C++","Hadoop","Spark","Tensorflow","Pytorch","MLFlow","Natural Language Processing","Graph Analysis"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:54:24.814Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Toronto, ON, CA"}},"employmentType":"INTERN","occupationalCategory":"Engineering","industry":"Technology","skills":"Machine Learning, Artificial Intelligence, Python, Java, C++, Hadoop, Spark, Tensorflow, Pytorch, MLFlow, Natural Language Processing, Graph Analysis","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":6000,"maxValue":9500,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_b372d3eb-ee1"},"title":"Staff Research Engineer, Applied AI","description":"<p>We are seeking a Staff Research Engineer, Applied AI to lead the development and deployment of novel applications, leveraging Google&#39;s generative AI models.</p>\n<p>This role focuses on rapidly developing new features, and working across partner teams to deliver solutions, and maximize impact for Google and top customers.</p>\n<p>You will be instrumental in translating cutting-edge AI research into real-world products, and demonstrating the capabilities of latest-generation models.</p>\n<p>We are looking for engineers with a strong track record of building and shipping AI-powered software, ideally with experience in early-stage environments where they have contributed to scaling products from initial concept to production.</p>\n<p>The ideal candidate will be motivated by the opportunity to drive product &amp; 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Specifically, you will:</p>\n<ul>\n<li>Invent, design and implement RL environments and evaluations.</li>\n<li>Conduct experiments and shape our research roadmap.</li>\n<li>Deliver your work into training runs.</li>\n<li>Collaborate with other researchers, engineers, and performance engineering specialists across and outside Anthropic.</li>\n</ul>\n<p>We&#39;re looking for someone with expertise in accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch), and experience with balancing research exploration with engineering implementation.</p>\n<p>Strong candidates may also have experience with reinforcement learning, porting ML workloads between different types of accelerators, and familiarity with LLM training methodologies.</p>\n<p>The annual compensation range for this role is $350,000-$850,000 USD.</p>\n<p>Please note that we&#39;re an extremely collaborative group, and we value communication skills. The easiest way to understand our research directions is to read our recent research.</p>\n<p>We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_c9ab5cbc-dd6","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5160330008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000-$850,000 USD","x-skills-required":["accelerator performance","ML framework programming","reinforcement learning","RL environments and evaluations","experiments and research roadmap","training runs","collaboration with researchers and engineers"],"x-skills-preferred":["CUDA","ROCm","Triton","Pallas","JAX","PyTorch","LLM training methodologies"],"datePosted":"2026-04-18T15:54:02.762Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"accelerator performance, ML framework programming, reinforcement learning, RL environments and evaluations, experiments and research roadmap, training runs, collaboration with researchers and engineers, CUDA, ROCm, Triton, Pallas, JAX, PyTorch, LLM training methodologies","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":850000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_43ed459a-4da"},"title":"Machine Learning Engineer, Support Experience","description":"<p><strong>Job Role</strong></p>\n<p>As a Machine Learning Engineer on the Support Experience team, you&#39;ll play a crucial role in enhancing our self-serve support experiences.</p>\n<p><strong>About the Team</strong></p>\n<p>The Support Experience engineering organization builds and improves Stripe&#39;s user support from end to end: how users get help within our products, how they get in touch with us when they have questions, and how our teams use internal tools to answer those questions.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Design and implement state-of-the-art ML models and large-scale ML systems for enhancing self-serve support capabilities, balancing ML principles, domain knowledge, and engineering constraints</li>\n<li>Develop and optimize contextual conversation models and ML-powered resolution flows for common support scenarios, using tools such as PyTorch, TensorFlow, and XGBoost</li>\n<li>Create and refine pipelines for training and evaluating models in both offline and online environments, with a focus on improving support quality and user satisfaction</li>\n<li>Implement ML features that streamline information collection and processing for support agents, enhancing overall support efficiency</li>\n<li>Collaborate with product, strategy, and content teams to propose, prioritize, and implement new AI-driven support features and improve answer capabilities</li>\n</ul>\n<p><strong>Requirements</strong></p>\n<ul>\n<li>Bachelor&#39;s Degree in ML/AI or related field (e.g. math, physics, statistics)</li>\n<li>3+ years in AI/ML and backend engineering, including building and operating production ML systems at global scale with stringent SLOs,balancing reliability, latency, and cost,with privacy, security, and compliance by design.</li>\n<li>Deep and up-to-date applied LLM experience: RAG/embeddings, tool use/function calling, agentic planning/orchestration architectures, post-training methods, code generation, benchmarks and evaluations, etc.</li>\n<li>Familiarity with classical ML methods and common frameworks e.g. Pytorch, TensorFlow.</li>\n<li>Proficient in Python; strong distributed systems and data science fundamentals.</li>\n<li>Experience working closely with product management, design, other engineers, and other cross-functional partners.</li>\n<li>Strong technical leadership and communication: mentoring and elevating engineers, elevating AI/ML awareness and posture within organizations, setting architectural direction, and driving alignment in ambiguity.</li>\n</ul>\n<p><strong>Preferred Qualifications</strong></p>\n<ul>\n<li>MS/PhD degree in ML/AI or related field (e.g. math, physics, statistics)</li>\n<li>Experience working in Java or Ruby codebases</li>\n<li>Experience designing, deploying, and owning Agentic LLM solutions (e.g., multi-step orchestrators, tool use/function calling) specifically for complex customer support or internal workflow automation.</li>\n<li>Comfortable working with distributed teams across multiple locations and time zones</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_43ed459a-4da","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Stripe","sameAs":"https://stripe.com/","logo":"https://logos.yubhub.co/stripe.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/stripe/jobs/7813942","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["ML/AI","Backend Engineering","PyTorch","TensorFlow","Python","Distributed Systems","Data Science"],"x-skills-preferred":["LLM","Agentic Planning","Orchestration Architectures","Post-Training Methods","Code Generation","Benchmarks and Evaluations"],"datePosted":"2026-04-18T15:53:43.127Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Toronto, Canada"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"ML/AI, Backend Engineering, PyTorch, TensorFlow, Python, Distributed Systems, Data Science, LLM, Agentic Planning, Orchestration Architectures, Post-Training Methods, Code Generation, Benchmarks and Evaluations"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_d70a8194-b84"},"title":"Software Engineer, Machine Learning","description":"<p>We are seeking a versatile and experienced Machine Learning / AI Engineer to join our growing AI team, working at the intersection of applied machine learning, infrastructure, and product innovation. Your work will drive user productivity, shape new product experiences, and advance the state of AI at Figma.</p>\n<p>As a Machine Learning / AI Engineer, you will design, build, and productionize ML models for Search, Discovery, Ranking, Retrieval-Augmented Generation (RAG), and generative AI features. You will also build and maintain scalable data pipelines to collect high-quality training and evaluation datasets, including annotation systems and human-in-the-loop workflows.</p>\n<p>You will collaborate closely with engineers, researchers, designers, and product managers across multiple teams to deliver high-quality ML-driven features and infrastructure. This is a high-impact, cross-functional role where you will shape both foundational systems and user-facing capabilities.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Design, build, and productionize ML models for Search, Discovery, Ranking, Retrieval-Augmented Generation (RAG), and generative AI features.</li>\n<li>Build and maintain scalable data pipelines to collect high-quality training and evaluation datasets, including annotation systems and human-in-the-loop workflows.</li>\n<li>Collaborate with AI researchers to iterate on datasets, evaluation metrics, and model architectures to improve quality and relevance.</li>\n<li>Work with product engineers to define and deliver impactful AI features across Figma&#39;s platform.</li>\n<li>Partner with infrastructure engineers to develop and optimize systems for training, inference, monitoring, and deployment.</li>\n<li>Explore new ideas at the edge of what&#39;s technically possible and help shape the long-term AI vision at Figma.</li>\n</ul>\n<p>Requirements include:</p>\n<ul>\n<li>5+ years of industry experience in software engineering, with 3+ years focused on applied machine learning or AI.</li>\n<li>Strong experience with end-to-end ML model development, including training, evaluation, deployment, and monitoring.</li>\n<li>Proficiency in Python and familiarity with ML libraries like PyTorch, TensorFlow, Scikit-learn, Spark MLlib, or XGBoost.</li>\n<li>Experience designing and building scalable data and annotation pipelines, as well as evaluation systems for AI model quality.</li>\n<li>Experience mentoring or leading others and contributing to a culture of technical excellence and innovation.</li>\n</ul>\n<p>Preferred qualifications include:</p>\n<ul>\n<li>Familiarity with search relevance, ranking, NLP, or RAG systems.</li>\n<li>Experience with AI infrastructure and MLOps, including observability, CI/CD, and automation for ML workflows.</li>\n<li>Experience working on creative or design-focused ML applications.</li>\n<li>Knowledge of additional languages such as C++ or Go is a plus, but not required.</li>\n<li>A product mindset with the ability to tie technical work to user outcomes and business impact.</li>\n<li>Strong collaboration and communication skills, especially when working across functions (engineering, product, research).</li>\n</ul>\n<p>At Figma, one of our values is Grow as you go. We believe in hiring smart, curious people who are excited to learn and develop their skills. If you&#39;re excited about this role but your past experience doesn&#39;t align perfectly with the points outlined in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_d70a8194-b84","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Figma","sameAs":"https://www.figma.com/","logo":"https://logos.yubhub.co/figma.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/figma/jobs/5551532004","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$153,000-$376,000 USD","x-skills-required":["Machine Learning","AI","Python","PyTorch","TensorFlow","Scikit-learn","Spark MLlib","XGBoost","Data Pipelines","Annotation Systems","Human-in-the-loop Workflows"],"x-skills-preferred":["Search Relevance","Ranking","NLP","RAG Systems","AI Infrastructure","MLOps","Observability","CI/CD","Automation","Creative or Design-Focused ML Applications"],"datePosted":"2026-04-18T15:53:04.257Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA • New York, NY • United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Machine Learning, AI, Python, PyTorch, TensorFlow, Scikit-learn, Spark MLlib, XGBoost, Data Pipelines, Annotation Systems, Human-in-the-loop Workflows, Search Relevance, Ranking, NLP, RAG Systems, AI Infrastructure, MLOps, Observability, CI/CD, Automation, Creative or Design-Focused ML Applications","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":153000,"maxValue":376000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_dc17980d-461"},"title":"Research Engineer, Interpretability","description":"<p>JOB TITLE: Research Engineer, Interpretability \\n LOCATION: San Francisco, CA \\n DEPARTMENT: AI Research &amp; Engineering \\n \\n JOB DESCRIPTION: \\n \\n When you see what modern language models are capable of, do you wonder, &quot;How do these things work? How can we trust them?&quot; \\n \\n The Interpretability team at Anthropic is working to reverse-engineer how trained models work because we believe that a mechanistic understanding is the most robust way to make advanced systems safe. \\n \\n Think of us as doing &quot;neuroscience&quot; of neural networks using &quot;microscopes&quot; we build - or reverse-engineering neural networks like binary programs. \\n \\n More resources to learn about our work: \\n - Our research blog - covering advances including Monosemantic Features and Circuits \\n - An Introduction to Interpretability from our research lead, Chris Olah \\n - The Urgency of Interpretability from CEO Dario Amodei \\n - Engineering Challenges Scaling Interpretability - directly relevant to this role \\n - 60 Minutes segment - Around 8:07, see a demo of tooling our team built \\n - New Yorker article - what it&#39;s like to work on one of AI&#39;s hardest open problems \\n \\n Even if you haven&#39;t worked on interpretability before, the infrastructure expertise is similar to what&#39;s needed across the lifecycle of a production language model: \\n - Pretraining: Training dictionary learning models looks a lot like model pretraining - creating stable, performant training jobs for massively parameterized models across thousands of chips \\n - Inference: Interp runs a customized inference stack. Day-to-day analysis requires services that allow editing a model&#39;s internal activations mid-forward-pass - for example, adding a &quot;steering vector&quot; \\n - Performance: Like all LLM work, we push up against the limits of hardware and software. Rather than squeezing the last 0.1%, we are focused on finding bottlenecks, fixing them and moving ahead given rapidly evolving research and safety mission \\n \\n The science keeps scaling - and it&#39;s now applied directly in safety audits on frontier models, with real deadlines. As our research has matured, engineering and infrastructure have become a bottleneck. Your work will have a direct impact on one of the most important open problems in AI. \\n \\n RESPONSIBILITIES: \\n - Build and maintain the specialized inference and training infrastructure that powers interpretability research - including instrumented forward/backward passes, activation extraction, and steering vector application \\n - Resolve scaling and efficiency bottlenecks through profiling, optimization, and close collaboration with peer infrastructure teams \\n - Design tools, abstractions, and platforms that enable researchers to rapidly experiment without hitting engineering barriers \\n - Help bring interpretability research into production safety audits - with real deadlines and high reliability expectations \\n - Work across the stack - from model internals and accelerator-level optimization to user-facing research tooling \\n \\n YOU MAY BE A GOOD FIT IF YOU: \\n - Have 5-10+ years of experience building software \\n - Are highly proficient in at least one programming language (e.g., Python, Rust, Go, Java) and productive with Python \\n - Are extremely curious about unfamiliar domains; can quickly learn and put that knowledge to work, e.g. diving into new layers of the stack to find bottlenecks \\n - Have a strong ability to prioritize the most impactful work and are comfortable operating with ambiguity and questioning assumptions \\n - Prefer fast-moving collaborative projects to extensive solo efforts \\n - Are curious about interpretability research and its role in AI safety (though no research experience is required!) \\n - Care about the societal impacts and ethics of your work \\n - Are comfortable working closely with researchers, translating research needs into engineering solutions. \\n \\n STRONG CANDIDATES MAY ALSO HAVE EXPERIENCE WITH: \\n - Optimizing the performance of large-scale distributed systems \\n - Language modeling fundamentals with transformers \\n - High Performance LLM optimization: memory management, compute efficiency, parallelism strategies, inference throughput optimization \\n - Working hands-on in a mainstream ML stack - PyTorch/CUDA on GPUs or JAX/XLA on TPUs \\n - Collaborating closely with researchers and building tooling to support research teams; or directly performed research with complex engineering challenges \\n \\n REPRESENTATIVE PROJECTS: \\n - Building Garcon, a tool that allows researchers to easily instrument LLMs to extract internal activations \\n - Designing and optimizing a pipeline to efficiently collect petabytes of transformer activations and shuffle them \\n - Profiling and optimizing ML training jobs, including multi-GPU parallelism and memory optimization \\n - Building a steered inference system that applies targeted interventions to model internals at scale (conceptually similar to Golden Gate Claude but for safety research) \\n \\n ROLE SPECIFIC LOCATION POLICY: \\n - This role is based in the San Francisco office; however, we are open to considering exceptional candidates for remote work on a case-by-case basis. \\n \\n The annual compensation range for this role is listed below. \\n For sales roles, the range provided is the role&#39;s On Target Earnings (\\&quot;OTE\\&quot;) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. \\n Annual Salary:\\\\$315,000-\\\\$560,000 USD</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_dc17980d-461","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4980430008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$315,000-$560,000 USD","x-skills-required":["Python","Rust","Go","Java","PyTorch","CUDA","JAX","XLA","High Performance LLM optimization","memory management","compute efficiency","parallelism strategies","inference throughput optimization"],"x-skills-preferred":["large-scale distributed systems","language modeling fundamentals","transformers","collaborating closely with researchers","building tooling to support research teams"],"datePosted":"2026-04-18T15:53:01.682Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Rust, Go, Java, PyTorch, CUDA, JAX, XLA, High Performance LLM optimization, memory management, compute efficiency, parallelism strategies, inference throughput optimization, large-scale distributed systems, language modeling fundamentals, transformers, collaborating closely with researchers, building tooling to support research teams","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":315000,"maxValue":560000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_4ced2159-802"},"title":"Research, Vision Expertise","description":"<p>Thinking Machines Lab is seeking a researcher to join their team in San Francisco. The successful candidate will work on advancing the science of visual perception and multimodal learning. They will design architectures that fuse pixels and text, build datasets and evaluation methods that test real-world comprehension, and develop representations that let models ground abstract concepts in the physical world.</p>\n<p>The ideal candidate will have expertise in multimodality and experience running large-scale experiments. They will be comfortable contributing to complex engineering systems and have a strong grasp of probability, statistics, and machine learning fundamentals.</p>\n<p>This is an evergreen role, meaning that the position is open on an ongoing basis. The company receives many applications, and there may not always be an immediate role that aligns perfectly with the candidate&#39;s experience and skills. However, they encourage candidates to apply and continuously review applications.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Own research projects on training and performance analysis of multimodal AI models.</li>\n<li>Curate and build large-scale datasets and evaluation benchmarks to advance vision capabilities.</li>\n<li>Work with data infrastructure engineers, pretraining researchers and engineers, and product teams to create frontier multimodal models and the products that leverage them.</li>\n<li>Publish and present research that moves the entire community forward.</li>\n</ul>\n<p>Skills and Qualifications:</p>\n<ul>\n<li>Ability to design, run, and analyze experiments thoughtfully, with demonstrated research judgment and empirical rigor.</li>\n<li>Understanding of machine learning fundamentals, large-scale training, and distributed compute environments.</li>\n<li>Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX).</li>\n<li>Comfortable with debugging distributed training and writing code that scales.</li>\n<li>Bachelor&#39;s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.</li>\n</ul>\n<p>Preferred qualifications include research or engineering contributions in visual reasoning, spatial understanding, or multimodal architecture design, experience developing evaluation frameworks for multimodal tasks, publications or open-source contributions in vision-language modeling, video understanding, or multimodal AI, and a strong grasp of probability, statistics, and ML fundamentals.</p>\n<p>Logistics:</p>\n<ul>\n<li>Location: San Francisco, California.</li>\n<li>Compensation: $350,000 - $475,000 USD per year, depending on background, skills, and experience.</li>\n<li>Visa sponsorship: Yes.</li>\n<li>Benefits: Generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_4ced2159-802","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Thinking Machines Lab","sameAs":"https://thinkingmachines.ai/","logo":"https://logos.yubhub.co/thinkingmachines.ai.png"},"x-apply-url":"https://job-boards.greenhouse.io/thinkingmachines/jobs/5002288008","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000 - $475,000 USD per year","x-skills-required":["Python","Deep learning framework (e.g., PyTorch, TensorFlow, or JAX)","Machine learning fundamentals","Large-scale training","Distributed compute environments"],"x-skills-preferred":["Visual reasoning","Spatial understanding","Multimodal architecture design","Evaluation frameworks for multimodal tasks","Vision-language modeling","Video understanding","Multimodal AI"],"datePosted":"2026-04-18T15:52:43.848Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Deep learning framework (e.g., PyTorch, TensorFlow, or JAX), Machine learning fundamentals, Large-scale training, Distributed compute environments, Visual reasoning, Spatial understanding, Multimodal architecture design, Evaluation frameworks for multimodal tasks, Vision-language modeling, Video understanding, Multimodal AI","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":475000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_ed5725bb-311"},"title":"Applied Research Engineer, Agents","description":"<p>Shape the Future of AI</p>\n<p>At Labelbox, we&#39;re building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we&#39;ve been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially.</p>\n<p>As an Applied Research Engineer at Labelbox, you&#39;ll sit at the junction of advanced AI research and real product impact, with a focus on the data that makes modern agents work,browser interactions, SWE/code traces, GUI sessions, and multi-turn workflows. You&#39;ll drive the data landscape required to advance capable, adaptable agents and help shape Labelbox&#39;s strategy for collecting, synthesizing, and evaluating it.</p>\n<p>Create frameworks and tools to construct, train, benchmark and evaluate autonomous agent capabilities.</p>\n<p>Design agent-focused data programs using supervised fine-tuning (SFT) and reinforcement learning (RL) methodologies.</p>\n<p>Develop data pipelines from diverse sources like code repositories, web browsers, and computer systems.</p>\n<p>Implement and adapt popular open-source agent libraries and benchmarks with proprietary datasets and models.</p>\n<p>Engage with research teams in frontier AI labs and the wider AI community to understand evolving agent data needs for frontier models and share best practices.</p>\n<p>Collaborate closely with frontier AI lab customers to understand requirements and guide model development.</p>\n<p>Publish research findings in academic journals, conferences, and blog posts.</p>\n<p>What You Bring</p>\n<p>Ph.D. or Master&#39;s degree in Computer Science, Machine Learning, AI, or related field.</p>\n<p>At least 3 years of experience addressing sophisticated ML problems with successful delivery to customers.</p>\n<p>Experience building and training autonomous agents,tool use, structured outputs, multi-step planning,across browsers/GUI, codebases, and databases using SFT and RL.</p>\n<p>Constructed and evaluated agentic benchmarks (e.g. SWE-bench, WebArena, τ-bench, OSWorld) and reliability/efficiency suites (e.g. WABER).</p>\n<p>Adept at interpreting research literature and quickly turning new ideas into prototypes.</p>\n<p>Deep understanding of frontier models (autoregressive, diffusion), post-training (SFT, RLVR, RLAIF, RLHF, et al.), and their human data requirements.</p>\n<p>Proficient in Python, data science libraries and deep learning frameworks (e.g., PyTorch, JAX, TensorFlow).</p>\n<p>Strong analytical and problem-solving abilities in ambiguous situations.</p>\n<p>Excellent communication skills.</p>\n<p>Track record of publications in top-tier AI/ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, etc.).</p>\n<p>Labelbox Applied Research</p>\n<p>At Labelbox Applied Research, we&#39;re committed to pushing the boundaries of AI and data-centric machine learning, with a particular focus on advanced human-AI interaction techniques. We believe that high-quality human data and sophisticated human feedback integration methods are key to unlocking the next generation of AI capabilities. Our research team works at the intersection of machine learning, human-computer interaction, and AI ethics to develop innovative solutions that can be practically applied in real-world scenarios.</p>\n<p>Life at Labelbox</p>\n<p>Location: Join our dedicated tech hubs in San Francisco or Wrocław, Poland</p>\n<p>Work Style: Hybrid model with 2 days per week in office, combining collaboration and flexibility</p>\n<p>Environment: Fast-paced and high-intensity, perfect for ambitious individuals who thrive on ownership and quick decision-making</p>\n<p>Growth: Career advancement opportunities directly tied to your impact</p>\n<p>Vision: Be part of building the foundation for humanity&#39;s most transformative technology</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_ed5725bb-311","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Labelbox","sameAs":"https://www.labelbox.com/","logo":"https://logos.yubhub.co/labelbox.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/labelbox/jobs/4829775007","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$250,000-$300,000 USD","x-skills-required":["Python","data science libraries","deep learning frameworks","PyTorch","JAX","TensorFlow","supervised fine-tuning","reinforcement learning","agent libraries","benchmarks","proprietary datasets","human-AI interaction","AI ethics"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:52:38.777Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco Bay Area"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, data science libraries, deep learning frameworks, PyTorch, JAX, TensorFlow, supervised fine-tuning, reinforcement learning, agent libraries, benchmarks, proprietary datasets, human-AI interaction, AI ethics","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":250000,"maxValue":300000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_34a04ec5-ae9"},"title":"Machine Learning Engineer II","description":"<p>We&#39;re looking for a Machine Learning Engineer II to join our Growth Platform engineering group. As a Machine Learning Engineer II, you will be responsible for developing and implementing ML models to improve user targeting and personalization for growth initiatives. You will design and build scalable ML pipelines for data processing, model training, and deployment. You will collaborate with cross-functional teams to identify potential ML solutions for growth opportunities. You will conduct A/B tests to evaluate the performance of ML models and optimize their impact on key growth metrics. You will analyze large datasets to extract insights and inform decision-making for user acquisition and retention strategies. You will contribute to the development of our ML infrastructure, ensuring it can support rapid experimentation and deployment. You will stay up-to-date with the latest advancements in ML and recommend new techniques to enhance our growth efforts. You will participate in code reviews and collaborate with team members as needed. You will thoughtfully leverage AI tools to speed up design, coding, debugging, and documentation, while applying your own critical thinking to validate outputs and explain how you used AI in your workflow. You will shape our AI-assisted engineering practices by sharing patterns, guardrails, and learnings with the team so we can safely increase our impact without compromising code quality, reliability, or candidate expectations.</p>\n<p>To be successful in this role, you will need to have 3+ years of experience applying ML to real-world problems, preferably in a growth or user acquisition context. You will need to have excellent communication skills and the ability to work effectively in cross-functional teams. You will need to have strong problem-solving skills and the ability to translate business requirements into technical solutions. You will need to have strong programming skills in Python and experience with PyTorch. You will need to have proficiency in data processing and analysis using tools like SQL, Spark, or Hadoop. You will need to have experience with recommendation systems, user modeling, or personalization algorithms. You will need to have familiarity with statistical analysis. You will need to have experience using AI coding assistants and agentic tools as a force-multiplier, and equally comfortable solving problems from first principles when those tools aren’t available. You will need to have a Bachelor’s/Master’s degree in a relevant field or equivalent experience.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_34a04ec5-ae9","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Pinterest","sameAs":"https://www.pinterest.com/","logo":"https://logos.yubhub.co/pinterest.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/pinterest/jobs/7681666","x-work-arrangement":"hybrid","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Python","PyTorch","SQL","Spark","Hadoop","Recommendation systems","User modeling","Personalization algorithms","Statistical analysis","AI coding assistants"],"x-skills-preferred":["Natural Language Processing","Data visualization","Cloud platforms","Containerization technologies"],"datePosted":"2026-04-18T15:52:32.389Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Dublin, IE"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, PyTorch, SQL, Spark, Hadoop, Recommendation systems, User modeling, Personalization algorithms, Statistical analysis, AI coding assistants, Natural Language Processing, Data visualization, Cloud platforms, Containerization technologies"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_19c6b9e4-ff6"},"title":"Foundation and generative models for biomolecules","description":"<p>At Inceptive, you will drive forward development that could help billions of people. You will be part of a collaborative, interdisciplinary team building our biological software.</p>\n<p>The design space of biomolecules is unimaginably vast , far beyond what can be explored experimentally. Yet within this space lie molecules with properties essential for new medicines. Our machine learning models learn to design therapeutic biomolecules with specific, desirable functions.</p>\n<p>We advance the state of the art in molecular design by training large-scale foundation models and developing cutting-edge generative approaches. The models learn from diverse heterogeneous datasets and are refined through focused fine-tuning and feedback from experiments. Key to progress is a team that combines exceptional machine learning expertise with thorough domain understanding.</p>\n<p>You will collaborate closely with other machine learning researchers and engineers, as well as computational and experimental biologists, to advance these models and translate their capabilities into real therapeutic designs.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Embody our vision of an interdisciplinary environment and embrace learning about areas outside of your traditional area of expertise</li>\n</ul>\n<ul>\n<li>Develop, implement, train, and iteratively improve state-of-the-art models for biomolecule design</li>\n</ul>\n<ul>\n<li>Analyze, visualize, and communicate results to support team efforts in improving models and data</li>\n</ul>\n<ul>\n<li>Create, deploy, and refine tools for efficient, reliable machine learning experimentation and production</li>\n</ul>\n<ul>\n<li>Work with biologists to collect data for the training and evaluation of generative models of biomolecules</li>\n</ul>\n<ul>\n<li>Provide mentorship and technical direction to team members as appropriate</li>\n</ul>\n<p><strong>Qualifications</strong></p>\n<ul>\n<li>3+ years of hands-on experience developing ML models</li>\n</ul>\n<ul>\n<li>Demonstrated track record of implementing, training, improving advanced machine learning models</li>\n</ul>\n<ul>\n<li>Highly capable programmer fluent in Python ecosystem and PyTorch or similar deep learning framework</li>\n</ul>\n<ul>\n<li>Availability to work with team members across US and Europe, with meetings starting at 8am PT and ending at 7pm CET</li>\n</ul>\n<ul>\n<li>Readiness to travel several times a year for company retreats and business events</li>\n</ul>\n<p><strong>Compensation</strong></p>\n<p>$200K – $275K + Bonus + Equity</p>\n<p><strong>Benefits</strong></p>\n<ul>\n<li>A competitive compensation package</li>\n</ul>\n<ul>\n<li>30 days paid vacation per year</li>\n</ul>\n<ul>\n<li>Comprehensive health insurance for US based employees</li>\n</ul>\n<ul>\n<li>401K with company match for US based employees and Direktversicherung for German employees</li>\n</ul>\n<ul>\n<li>Quarterly company-wide retreats</li>\n</ul>\n<ul>\n<li>Monthly wellness benefit</li>\n</ul>\n<ul>\n<li>Budget for multiple visits per year to our offices in Berlin, Palo Alto or Switzerland</li>\n</ul>\n<ul>\n<li>Learning &amp; Development budget to attend conferences, take courses, or otherwise invest in your professional growth, as well as access to the Learning &amp; Development platform EdX and Hone</li>\n</ul>\n<ul>\n<li>A buddy to help you get settled</li>\n</ul>\n<p>At Inceptive, we are creating tools to develop increasingly powerful biological software for the rational design of novel, broadly accessible medicines and biotechnologies previously out of reach. Our team brings together vast expertise in molecular biology, machine learning, and software engineering, and we are all working towards becoming interdisciplinary, meaning we deepen the knowledge we have in our area of expertise while also expanding our knowledge of completely new fields.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_19c6b9e4-ff6","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Inceptive","sameAs":"https://inceptive.com","logo":"https://logos.yubhub.co/inceptive.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/inceptive/jobs/4961579007","x-work-arrangement":"onsite","x-experience-level":"entry|mid|senior|staff|executive","x-job-type":"full-time","x-salary-range":"$200K – $275K + Bonus + Equity","x-skills-required":["Python","PyTorch","Machine Learning","Deep Learning","Biological Software","Molecular 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of diverse perspectives to solve complex problems.</p>\n<p>Collaboration will be key as you work alongside our engineering, design, and product teams to build groundbreaking agentic applications.</p>\n<p>If you&#39;re passionate about agentic and multimodal AI and contributing to a team that&#39;s changing the face of business communications, you&#39;ll find yourself right at home with us.</p>\n<p>This position reports to the Senior Manager of the NLP team and has the opportunity to be based in Vancouver, BC.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Research and develop state-of-the-art algorithms for autonomous voice agents, specifically focusing on real-time speech processing and reasoning loops.</li>\n</ul>\n<ul>\n<li>Advance DialpadGPT: Design and execute distributed training strategies to optimize our proprietary LLMs for agentic behaviors, including precise tool use, instruction following, and latency-constrained generation.</li>\n</ul>\n<ul>\n<li>Conduct rigorous evaluation and monitoring of model performances and troubleshoot issues with a keen understanding of resultant business impacts.</li>\n</ul>\n<ul>\n<li>Design and implement orchestration layers that effectively chain LLMs with external tools and APIs to solve complex customer problems autonomously.</li>\n</ul>\n<ul>\n<li>Work with large-scale multimodal datasets (text, audio) to improve model robustness and alignment.</li>\n</ul>\n<ul>\n<li>Collaborate with engineering, product, and design teams to deploy scalable, low-latency models and algorithms in production.</li>\n</ul>\n<ul>\n<li>Submit papers to top-tier academic conferences (ACL, EMNLP, NeurIPS) and contribute to the team’s research culture.</li>\n</ul>\n<p>To succeed in this role, you&#39;ll need:</p>\n<ul>\n<li>A Master’s or PhD degree in Computer Science, Machine Learning, Computational Linguistics, or a related quantitative field.</li>\n</ul>\n<ul>\n<li>2+ years of industry experience in Machine Learning/NLP for Master’s degree holders, or 1+ years for PhD holders.</li>\n</ul>\n<ul>\n<li>Deep understanding of LLMs: Demonstrated experience with training, fine-tuning (PEFT/LoRA), and alignment techniques (RLHF/DPO) for specific domains or tasks.</li>\n</ul>\n<ul>\n<li>Experience with Agentic Systems: Familiarity with building autonomous agents, including concepts like tool use, function calling, reasoning chains (CoT), and memory management.</li>\n</ul>\n<ul>\n<li>Strong proficiency in Python and PyTorch, with the ability to write clean, production-ready research code.</li>\n</ul>\n<ul>\n<li>Research Track Record: A history of publishing in top-tier conferences (ACL, EMNLP, NeurIPS, ICASSP) is highly valued.</li>\n</ul>\n<ul>\n<li>Multimodal Awareness: Familiarity with speech technologies (ASR, TTS) or processing real-time audio streams is a strong plus.</li>\n</ul>\n<ul>\n<li>Ability to bridge the gap between research and product, translating complex technical concepts into business value.</li>\n</ul>\n<ul>\n<li>Familiarity with version control tools like Git for collaborative projects.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_d3b1dbb2-6ce","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Dialpad","sameAs":"https://dialpad.com","logo":"https://logos.yubhub.co/dialpad.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/dialpad/jobs/8508615002","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$161,500-$191,500 CAD","x-skills-required":["Machine Learning","Natural Language Processing","Python","PyTorch","LLMs","Agentic Systems","Speech Technologies","Version Control Tools"],"x-skills-preferred":["Research Track Record","Multimodal Awareness","Ability to Bridge Research and 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We are also developing real-time knowledge retrieval models to power live coaching features for customer support and sales agents.</p>\n<p>Beyond the technical skills, we are a team that values collaboration, continuous learning, and the application of diverse perspectives to solve complex problems. 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As a member of this team, you will play a key role in building and maintaining our open-source AI/ML platforms to enable users to train, deploy and monitor models and GenAI agents at scale.</p>\n<p>The impact you&#39;ll have:</p>\n<ul>\n<li>Design and implement platform capabilities to support the AI/ML development and productionization lifecycle including training, evaluation, deployment, monitoring, and management of models and agents</li>\n<li>Design and implement platform integrations with various frameworks in the AI/ML ecosystem</li>\n<li>Collaborate with the AI/ML community across the world to advance the state-of-the-art in AIOps</li>\n<li>Ensure the latest AI/ML tooling advancements are available to Databricks&#39; customers, thereby enabling organizations around the world to get more value from their data</li>\n<li>Mentor and guide junior engineers on the team by helping with project planning, technical decisions, and code and document review</li>\n</ul>\n<p>What we look for:</p>\n<ul>\n<li>BS (or higher) in Computer Science, or a related field</li>\n<li>3+ years of hands-on experience in building production systems using at least one of the following programming languages: Python (Preferred), Scala and Java</li>\n<li>Experience building and maintaining software tools and frameworks for AI/ML, ideally in an open-source environment</li>\n<li>Familiarity with AI/ML and AIOps concepts and technologies, such as model training, deployment, and monitoring</li>\n<li>Deep understanding and experience in working with agent frameworks such as LangChain, LlamaIndex, DSPy, or other similar projects</li>\n<li>Significant contributions to open-source projects in the AI/ML domain, such as SparkML, TensorFlow, PyTorch, MLflow, or other similar projects</li>\n</ul>\n<p>About Databricks</p>\n<p>Databricks is the data and AI company. More than 10,000 organizations worldwide , including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 , rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI.</p>\n<p>Benefits</p>\n<p>At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. 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The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles.</p>\n<p>Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location.</p>\n<p>Based on the factors above, Databricks anticipates utilizing the full width of the range.</p>\n<p>The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above.</p>\n<p>For more information regarding which range your location is in visit our page here.</p>\n<p>Local Pay Range $180,656-$248,360 USD</p>\n<p>Benefits:</p>\n<p>At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees.</p>\n<p>For specific details on the benefits offered in your region click here.</p>\n<p>Our Commitment to Diversity and Inclusion:</p>\n<p>At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel.</p>\n<p>We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards.</p>\n<p>Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.</p>\n<p>Compliance:</p>\n<p>If access to export-controlled technology or source code is required for performance of job duties, it is within Employer&#39;s discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_c1802213-f81","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Databricks","sameAs":"https://databricks.com","logo":"https://logos.yubhub.co/databricks.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/databricks/jobs/8415203002","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["GenAI","HuggingFace","LangChain","DSPy","pandas","scikit-learn","PyTorch","AWS","Azure","GCP","Graduate degree in Computer Science, Engineering, Statistics, Operations Research, etc."],"x-skills-preferred":[],"datePosted":"2026-04-18T15:47:10.995Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Maryland; Virginia; Washington, D.C."}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"GenAI, HuggingFace, LangChain, DSPy, pandas, scikit-learn, PyTorch, AWS, Azure, GCP, Graduate degree in Computer Science, Engineering, Statistics, Operations Research, etc."},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_3b01c809-8ef"},"title":"Staff Machine Learning Systems Engineer","description":"<p>As a Staff Machine Learning Systems Engineer at Reddit, you will lead the development of a platform for large-scale ML models. 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How can we trust them?&quot; The Interpretability team at Anthropic is working to reverse-engineer how trained models work because we believe that a mechanistic understanding is the most robust way to make advanced systems safe.</p>\n<p>Think of us as doing &quot;neuroscience&quot; of neural networks using &quot;microscopes&quot; we build - or reverse-engineering neural networks like binary programs.</p>\n<p>More resources to learn about our work:</p>\n<ul>\n<li>Our research blog - covering advances including Monosemantic Features and Circuits</li>\n</ul>\n<ul>\n<li>An Introduction to Interpretability from our research lead, Chris Olah</li>\n</ul>\n<ul>\n<li>The Urgency of Interpretability from CEO Dario Amodei</li>\n</ul>\n<ul>\n<li>Engineering Challenges Scaling Interpretability - directly relevant to this role</li>\n</ul>\n<ul>\n<li>60 Minutes segment - Around 8:07, see a demo of tooling our team built</li>\n</ul>\n<ul>\n<li>New Yorker article - what it&#39;s like to work on one of AI&#39;s hardest open problems</li>\n</ul>\n<p>Even if you haven&#39;t worked on interpretability before, the infrastructure expertise is similar to what&#39;s needed across the lifecycle of a production language model:</p>\n<ul>\n<li>Pretraining: Training dictionary learning models looks a lot like model pretraining - creating stable, performant training jobs for massively parameterized models across thousands of chips</li>\n</ul>\n<ul>\n<li>Inference: Interp runs a customized inference stack. Day-to-day analysis requires services that allow editing a model&#39;s internal activations mid-forward-pass - for example, adding a &quot;steering vector&quot;</li>\n</ul>\n<ul>\n<li>Performance: Like all LLM work, we push up against the limits of hardware and software. Rather than squeezing the last 0.1%, we are focused on finding bottlenecks, fixing them and moving ahead given rapidly evolving research and safety mission</li>\n</ul>\n<p>The science keeps scaling - and it&#39;s now applied directly in safety audits on frontier models, with real deadlines. As our research has matured, engineering and infrastructure have become a bottleneck. Your work will have a direct impact on one of the most important open problems in AI.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Build and maintain the specialized inference and training infrastructure that powers interpretability research - including instrumented forward/backward passes, activation extraction, and steering vector application</li>\n</ul>\n<ul>\n<li>Resolve scaling and efficiency bottlenecks through profiling, optimization, and close collaboration with peer infrastructure teams</li>\n</ul>\n<ul>\n<li>Design tools, abstractions, and platforms that enable researchers to rapidly experiment without hitting engineering barriers</li>\n</ul>\n<ul>\n<li>Help bring interpretability research into production safety audits - with real deadlines and high reliability expectations</li>\n</ul>\n<ul>\n<li>Work across the stack - from model internals and accelerator-level optimization to user-facing research tooling</li>\n</ul>\n<p>You may be a good fit if you:</p>\n<ul>\n<li>Have 5-10+ years of experience building software</li>\n</ul>\n<ul>\n<li>Are highly proficient in at least one programming language (e.g., Python, Rust, Go, Java) and productive with Python</li>\n</ul>\n<ul>\n<li>Are extremely curious about unfamiliar domains; can quickly learn and put that knowledge to work, e.g. diving into new layers of the stack to find bottlenecks</li>\n</ul>\n<ul>\n<li>Have a strong ability to prioritize the most impactful work and are comfortable operating with ambiguity and questioning assumptions</li>\n</ul>\n<ul>\n<li>Prefer fast-moving collaborative projects to extensive solo efforts</li>\n</ul>\n<ul>\n<li>Are curious about interpretability research and its role in AI safety (though no research experience is required!)</li>\n</ul>\n<ul>\n<li>Care about the societal impacts and ethics of your work</li>\n</ul>\n<ul>\n<li>Are comfortable working closely with researchers, translating research needs into engineering solutions.</li>\n</ul>\n<p>Strong candidates may also have experience with:</p>\n<ul>\n<li>Optimizing the performance of large-scale distributed systems</li>\n</ul>\n<ul>\n<li>Language modeling fundamentals with transformers</li>\n</ul>\n<ul>\n<li>High Performance LLM optimization: memory management, compute efficiency, parallelism strategies, inference throughput optimization</li>\n</ul>\n<ul>\n<li>Working hands-on in a mainstream ML stack - PyTorch/CUDA on GPUs or JAX/XLA on TPUs</li>\n</ul>\n<ul>\n<li>Collaborating closely with researchers and building tooling to support research teams; or directly performed research with complex engineering challenges</li>\n</ul>\n<p>Representative Projects:</p>\n<ul>\n<li>Building Garcon, a tool that allows researchers to easily instrument LLMs to extract internal activations</li>\n</ul>\n<ul>\n<li>Designing and optimizing a pipeline to efficiently collect petabytes of transformer activations and shuffle them</li>\n</ul>\n<ul>\n<li>Profiling and optimizing ML training jobs, including multi-GPU parallelism and memory optimization</li>\n</ul>\n<ul>\n<li>Building a steered inference system that applies targeted interventions to model internals at scale (conceptually similar to Golden Gate Claude but for safety research)</li>\n</ul>\n<p>Role Specific Location Policy:</p>\n<ul>\n<li>This role is based in the San Francisco office; however, we are open to considering exceptional candidates for remote work on a case-by-case basis.</li>\n</ul>\n<p>The annual compensation range for this role is listed below.</p>\n<p>For sales roles, the range provided is the role&#39;s On Target Earnings (&quot;OTE&quot;) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.</p>\n<p>Annual Salary: $315,000-$560,000 USD</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_97212bdf-dd1","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4980430008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$315,000-$560,000 USD","x-skills-required":["Python","Rust","Go","Java","PyTorch","CUDA","JAX","XLA","Transformers","High Performance LLM optimization","Memory management","Compute efficiency","Parallelism strategies","Inference throughput optimization"],"x-skills-preferred":["Optimizing the performance of large-scale distributed systems","Language modeling fundamentals","Collaborating closely with researchers and building tooling to support research teams"],"datePosted":"2026-04-18T15:46:01.999Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Rust, Go, Java, PyTorch, CUDA, JAX, XLA, Transformers, High Performance LLM optimization, Memory management, Compute efficiency, Parallelism strategies, Inference throughput optimization, Optimizing the performance of large-scale distributed systems, Language modeling fundamentals, Collaborating closely with researchers and building tooling to support research teams","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":315000,"maxValue":560000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_fc38e24f-97e"},"title":"Senior Machine Learning Engineer","description":"<p>We&#39;re looking for a Senior Machine Learning Engineer to join our Ads Engineering team. As a key member of our team, you will design and build production ML systems that power core experiences across the platform, including personalized recommendations, search, and ranking systems, intelligent advertising systems, and large-scale machine learning pipelines.</p>\n<p>Our team is responsible for building systems that operate at internet scale and directly influence user experience, advertiser value, and business outcomes. You will work on high-impact systems that improve ranking, recommendations, search relevance, prediction, content/user understanding, and optimization systems.</p>\n<p>As a Senior Machine Learning Engineer, you will:</p>\n<ul>\n<li>Design, build, and deploy production-grade machine learning models and systems at scale</li>\n<li>Own the full ML lifecycle: from problem definition and feature engineering to training, evaluation, deployment, and monitoring</li>\n<li>Build scalable data and model pipelines with strong reliability, observability, and automated retraining</li>\n<li>Work with large-scale datasets to improve ranking, recommendations, search relevance, prediction, content/user understanding, and optimization systems</li>\n<li>Partner cross-functionally with Product, Data Science, Infrastructure, and Engineering teams to translate complex problems into ML solutions</li>\n<li>Improve system performance across latency, throughput, and model quality metrics</li>\n<li>Research and apply state-of-the-art machine learning and AI techniques, including deep learning, graph &amp; transformers based, and LLM evaluation/alignment</li>\n</ul>\n<p>Basic Qualifications:</p>\n<ul>\n<li>3-5+ years of experience building, deploying, and operating machine learning systems in production</li>\n<li>Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals</li>\n<li>ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs)</li>\n<li>Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow)</li>\n<li>Experience designing scalable ML pipelines, data processing systems, and model serving infrastructure</li>\n<li>Ability to work cross-functionally and translate ambiguous product or business problems into technical solutions</li>\n<li>Experience improving measurable metrics through applied machine learning</li>\n</ul>\n<p>Preferred Qualifications:</p>\n<ul>\n<li>Experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systems</li>\n<li>Familiarity with distributed systems and large-scale data processing (Spark, Kafka, Ray, Airflow, BigQuery, Redis, etc.)</li>\n<li>Experience working with real-time systems and low-latency production environments</li>\n<li>Background in feature engineering, model optimization, and production monitoring</li>\n<li>Experience with LLM/Gen AI techniques, including but not limited to LLM evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems and productionizing LLM-powered products at scale</li>\n<li>Advanced degree in Computer Science, Machine Learning, or related quantitative field</li>\n</ul>\n<p>Potential Teams:</p>\n<ul>\n<li>Ads Measurement Modeling</li>\n<li>Ads Targeting and Retrieval</li>\n<li>Advertiser Optimization</li>\n<li>Ads Marketplace Quality</li>\n<li>Ads Creative Effectiveness</li>\n<li>Ads Foundational Representations</li>\n<li>Ads Content Understanding</li>\n<li>Ads Ranking</li>\n<li>Feed Relevance</li>\n<li>Search and Answers Relevance</li>\n<li>ML Understanding</li>\n<li>Notifications Relevance</li>\n</ul>\n<p>Benefits:</p>\n<ul>\n<li>Comprehensive Healthcare Benefits and Income Replacement Programs</li>\n<li>401k with Employer Match</li>\n<li>Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support</li>\n<li>Family Planning Support</li>\n<li>Gender-Affirming Care</li>\n<li>Mental Health &amp; Coaching Benefits</li>\n<li>Flexible Vacation &amp; Paid Volunteer Time Off</li>\n<li>Generous Paid Parental Leave</li>\n</ul>\n<p>Pay Transparency:</p>\n<p>This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/. To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below. The base salary range for this position is $216,700-$303,400 USD</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_fc38e24f-97e","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Reddit","sameAs":"https://www.redditinc.com","logo":"https://logos.yubhub.co/redditinc.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/reddit/jobs/6960831","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$216,700-$303,400 USD","x-skills-required":["Python","Java","Go","PyTorch","TensorFlow","XGBoost","Random Forests","Regressions","Transformers","CNNs","GNNs","Spark","Kafka","Ray","Airflow","BigQuery","Redis"],"x-skills-preferred":["Recommender systems","Search/ranking systems","Advertising/auction systems","Large-scale representation learning","Multimodal embedding systems","Distributed systems","Large-scale data processing","Real-time systems","Low-latency production environments","Feature engineering","Model optimization","Production monitoring","LLM/Gen AI techniques","LLM evaluation","Alignment","Fine-tuning","Knowledge distillation","RAG/agentic systems"],"datePosted":"2026-04-18T15:45:58.533Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote - United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Java, Go, PyTorch, TensorFlow, XGBoost, Random Forests, Regressions, Transformers, CNNs, GNNs, Spark, Kafka, Ray, Airflow, BigQuery, Redis, Recommender systems, Search/ranking systems, Advertising/auction systems, Large-scale representation learning, Multimodal embedding systems, Distributed systems, Large-scale data processing, Real-time systems, Low-latency production environments, Feature engineering, Model optimization, Production monitoring, LLM/Gen AI techniques, LLM evaluation, Alignment, Fine-tuning, Knowledge distillation, RAG/agentic systems","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":216700,"maxValue":303400,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_f3dfc09f-532"},"title":"AI Engineer - FDE (Forward Deployed Engineer)","description":"<p>We are seeking an AI Engineer - FDE (Forward Deployed Engineer) to join our team. As an AI Engineer, you will develop cutting-edge GenAI solutions, incorporating the latest techniques from our Mosaic AI research to solve customer problems. You will own production rollouts of consumer and internally facing GenAI applications, serve as a trusted technical advisor to customers across a variety of domains, and present at conferences such as Data + AI Summit.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Develop cutting-edge GenAI solutions, incorporating the latest techniques from our Mosaic AI research to solve customer problems</li>\n<li>Own production rollouts of consumer and internally facing GenAI applications</li>\n<li>Serve as a trusted technical advisor to customers across a variety of domains</li>\n<li>Present at conferences such as Data + AI Summit</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>Experience building GenAI applications, including RAG, multi-agent systems, Text2SQL, fine-tuning, etc., with tools such as HuggingFace, LangChain, and DSPy</li>\n<li>Expertise in deploying production-grade GenAI applications, including evaluation and optimizations</li>\n<li>Extensive years of hands-on industry data science experience, leveraging common machine learning and data science tools, i.e. pandas, scikit-learn, PyTorch, etc.</li>\n<li>Experience building production-grade machine learning deployments on AWS, Azure, or GCP</li>\n<li>Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience</li>\n<li>Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike</li>\n<li>Passion for collaboration, life-long learning, and driving business value through AI</li>\n</ul>\n<p>Benefits: At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region, please click here.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_f3dfc09f-532","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Databricks","sameAs":"https://databricks.com/","logo":"https://logos.yubhub.co/databricks.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/databricks/jobs/8024004002","x-work-arrangement":"onsite","x-experience-level":"all levels","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["GenAI","HuggingFace","LangChain","DSPy","pandas","scikit-learn","PyTorch","AWS","Azure","GCP"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:45:26.463Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, United Kingdom"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"GenAI, HuggingFace, LangChain, DSPy, pandas, scikit-learn, PyTorch, AWS, Azure, GCP"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_a317d234-6b0"},"title":"Data Scientist, Ads","description":"<p>We are looking for a highly motivated and experienced Data Scientist to join our growing Ads Data Science team. As a Data Scientist, you will play a key role in developing as well as applying cutting-edge DS models/methods to improve our understanding of the dynamics that drive the success of our advertising platform, and identify opportunities to accelerate that success.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Analyze large-scale datasets to identify trends, patterns, and insights that can be used to improve the effectiveness of our advertising platform</li>\n<li>Develop ML models &amp; DS methods to for improved anomaly detection, prediction, pattern recognition</li>\n<li>Communicate findings and recommendations to stakeholders across the organization</li>\n<li>Collaborate with product, engineering, sales, and marketing partners to define product and program requirements and translate them into data science solutions</li>\n<li>Stay up-to-date on the latest advancements in machine learning and data science</li>\n</ul>\n<p>Qualifications:</p>\n<ul>\n<li>Advanced degree (Masters or Ph.D.) in a quantitative field such as: Statistics, Mathematics, Physics, Economics, or Operations Research</li>\n<li>For M.S. holders: 3+ years of industry experience in applied science or data science roles</li>\n<li>For Ph.D. holders: 2+ years of industry experience in applied science or data science roles</li>\n<li>Strong understanding of statistical modeling, machine learning algorithms, causal inference and experimental design</li>\n<li>Experience with large-scale data processing and analysis using tools such as Spark, Hadoop, or Hive; knowledge of BigQuery a plus</li>\n<li>Proficiency in Python or R and experience with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch</li>\n<li>Experience with SQL and relational databases</li>\n<li>Excellent communication and presentation skills</li>\n</ul>\n<p>Bonus Points:</p>\n<ul>\n<li>Experience with online advertising and ad tech</li>\n<li>Experience with causal inference and A/B testing</li>\n<li>Contributions to open-source projects or publications in relevant conferences or journals</li>\n</ul>\n<p>Benefits:</p>\n<ul>\n<li>Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support</li>\n<li>Family Planning Support</li>\n<li>Gender-Affirming Care</li>\n<li>Mental Health &amp; Coaching Benefits</li>\n<li>Comprehensive Medical Benefits &amp; Health Care Spending Account</li>\n<li>Registered Retirement Savings Plan with matching contributions</li>\n<li>Income Replacement Programs</li>\n<li>Flexible Vacation &amp; Paid Volunteer Time Off</li>\n<li>Generous Paid Parental Leave</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_a317d234-6b0","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Reddit","sameAs":"https://www.redditinc.com","logo":"https://logos.yubhub.co/redditinc.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/reddit/jobs/7607124","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["statistical modeling","machine learning algorithms","causal inference","experimental design","large-scale data processing","Spark","Hadoop","BigQuery","Python","R","scikit-learn","TensorFlow","PyTorch","SQL","relational databases"],"x-skills-preferred":["online advertising","ad tech","A/B testing","open-source projects","publications"],"datePosted":"2026-04-18T15:45:22.663Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote - British Columbia, Canada"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"statistical modeling, machine learning algorithms, causal inference, experimental design, large-scale data processing, Spark, Hadoop, BigQuery, Python, R, scikit-learn, TensorFlow, PyTorch, SQL, relational databases, online advertising, ad tech, A/B testing, open-source projects, publications"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_087e2e06-4fb"},"title":"Staff Machine Learning Engineer, Ads Auction (Ads Marketplace Quality)","description":"<p>We&#39;re looking for a Staff Machine Learning Engineer to join our Ads Marketplace Quality team. As a key member of the team, you will be responsible for developing and executing a vision to improve our Ads Marketplace at Reddit. You will develop a deep understanding of our marketplace dynamics and identify areas of improvement by getting to the bottom of data, design, implement and ship algorithms to production that improve our ads marketplace efficiency.</p>\n<p>In this role, you will specialize in improving and optimizing our ads auction and pricing mechanism which will have a direct impact on upleveling the utility for both our advertiser and user values. You will also have the opportunity to work on other org-wide strategic initiatives such as supply optimization and ad relevance, where you will drive and execute on Reddit’s vision to transform Reddit into an advertising platform that shows the right ads to the right users at the right time in the right context.</p>\n<p>As a Staff Machine Learning Engineer in the Ads Marketplace Quality team, you will be an industry technical leader with domain knowledge in ads marketplace dynamics, auction and pricing, you will research, formulate, and execute on our mission to build end-to-end algorithmic solutions and deliver values to all the three-sided participants to our marketplace.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Lead and oversee the strategy development, quarterly planning and day-to-day execution of initiatives related to ads marketplace, auction and pricing.</li>\n<li>Proactively further our understanding of marketplace dynamics and develop algorithms to improve the efficiency and effectiveness of our ads marketplace, auction and pricing.</li>\n<li>Oversee end-to-end ML workflows,from data ingestion and feature engineering to model training, evaluation, and deployment,that optimizes the ads marketplace efficiency.</li>\n<li>Be a mentor, lead both junior and senior engineers in implementing technical designs and reviews. Fostering a culture of innovation, technical excellence, and knowledge sharing across the organization.</li>\n<li>Be a cross-functional advocate for the team, collaborate with cross-functional teams (e.g., product management, data science, PMM, Sales etc.) to innovate and build products.</li>\n</ul>\n<p>Required Qualifications:</p>\n<ul>\n<li>8+ years of experience with industry-level product development, with at least 5+ years focused on data-driven, marketplace-optimization problem space at scale.</li>\n<li>Strong knowledge of ads marketplace optimization. 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Professional development funds</li>\n<li>Family Planning Support</li>\n<li>Flexible Vacation (please use them!) &amp; Reddit Global Wellness Days</li>\n<li>4+ months paid Parental Leave</li>\n<li>Paid Volunteer time off</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_087e2e06-4fb","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Reddit","sameAs":"https://www.redditinc.com","logo":"https://logos.yubhub.co/redditinc.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/reddit/jobs/7181821","x-work-arrangement":"remote","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$230,000-$322,000 USD","x-skills-required":["machine learning","ads marketplace optimization","large-scale data processing","distributed computing","data infrastructure","Spark","Kafka","Beam","Flink","TensorFlow","PyTorch","feature engineering","model training","inference","programming languages","statistical analysis","technical leadership","cross-functional settings","architectural decisions","influencing stakeholders"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:45:11.272Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote - United States"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"machine learning, ads marketplace optimization, large-scale data processing, distributed computing, data infrastructure, Spark, Kafka, Beam, Flink, TensorFlow, PyTorch, feature engineering, model training, inference, programming languages, statistical analysis, technical leadership, cross-functional settings, architectural decisions, influencing stakeholders","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":230000,"maxValue":322000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_c63000ba-f8b"},"title":"Senior Staff Machine Learning Engineer, Trust","description":"<p>We&#39;re looking for a Senior Staff Machine Learning Engineer to join our Trust team, which is responsible for developing the technology that helps protect our community and platform from fraud.</p>\n<p>As a senior technical individual contributor, you will partner closely with our leaders across the broader technical organization to design, execute and deliver in a complex and collaborative roadmap of Trust engineering efforts.</p>\n<p>Your expertise will be crucial in defining and executing on the long-term ML technical vision and strategy for the Trust organization, identifying key investments, architecting scalable solutions, and championing best practices that advance the state-of-the-art in production ML systems.</p>\n<p>You will serve as a technical leader and mentor to other ML and software engineers across the organization, providing guidance on complex architectural and modeling challenges, and raising the overall technical bar.</p>\n<p>Key responsibilities include:</p>\n<ul>\n<li>Defining and executing on the long-term ML technical vision and strategy for the Trust organization</li>\n<li>Serving as a technical leader and mentor to other ML and software engineers across the organization</li>\n<li>Driving and delivering large-scale, multi-quarter ML initiatives that span multiple teams</li>\n<li>Working with large scale structured and unstructured data, build and continuously improve cutting edge Machine Learning models for Airbnb product, business and operational use cases</li>\n<li>Collaborating with cross-functional partners to identify opportunities for business impact and understand, refine, and prioritize requirements for machine learning models</li>\n</ul>\n<p>Requirements include:</p>\n<ul>\n<li>12+ years of industry experience in applied Machine Learning</li>\n<li>2-3+ years working with LLMs and novel GenAI technologies</li>\n<li>Proficiency and proven experience on Agentic AI (frameworks, orchestration, architecture and productionization)</li>\n<li>A Bachelor’s, Master’s or PhD in CS/ML or related field</li>\n<li>Strong programming (Scala / Python / Java/ C++ or equivalent) and data engineering skills</li>\n<li>Deep understanding of Machine Learning best practices, algorithms, and domains</li>\n<li>Experience with AgenticAI, Tensorflow, PyTorch, Kubernetes, and industry experience building end-to-end Machine Learning and Agentic infrastructure</li>\n</ul>\n<p>Our Commitment To Inclusion &amp; Belonging: Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions.</p>\n<p>How We&#39;ll Take Care of You: Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. 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Your work will directly contribute to making AI systems more trustworthy and aligned with human values, ensuring our models operate safely as they become more capable.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Design and build scalable ML infrastructure to support real-time and batch classifier and safety evaluations across our model ecosystem</li>\n<li>Build monitoring and observability tools to track model performance, data quality, and system health for safety-critical applications</li>\n<li>Collaborate with research teams to productionize safety research, translating experimental safety techniques into robust, scalable systems</li>\n<li>Optimize inference latency and throughput for real-time safety evaluations while maintaining high reliability standards</li>\n<li>Implement automated testing, deployment, and rollback systems for ML models in production safety applications</li>\n<li>Partner with Safeguards, Security, and Alignment teams to understand requirements and deliver infrastructure that meets safety and production needs</li>\n<li>Contribute to the development of internal tools and frameworks that accelerate safety research and deployment</li>\n</ul>\n<p>You may be a good fit if you:</p>\n<ul>\n<li>Have 5+ years of experience building production ML infrastructure, ideally in safety-critical domains like fraud detection, content moderation, or risk assessment</li>\n<li>Are proficient in Python and have experience with ML frameworks like PyTorch, TensorFlow, or JAX</li>\n<li>Have hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes)</li>\n<li>Understand distributed systems principles and have built systems that handle high-throughput, low-latency workloads</li>\n<li>Have experience with data engineering tools and building robust data pipelines (e.g., Spark, Airflow, streaming systems)</li>\n<li>Are results-oriented, with a bias towards reliability and impact in safety-critical systems</li>\n<li>Enjoy collaborating with researchers and translating cutting-edge research into production systems</li>\n<li>Care deeply about AI safety and the societal impacts of your work</li>\n</ul>\n<p>Strong candidates may have experience with:</p>\n<ul>\n<li>Working with large language models and modern transformer architectures</li>\n<li>Implementing A/B testing frameworks and experimentation infrastructure for ML systems</li>\n<li>Developing monitoring and alerting systems for ML model performance and data drift</li>\n<li>Building automated labeling systems and human-in-the-loop workflows</li>\n<li>Experience in trust &amp; safety, fraud prevention, or content moderation domains</li>\n<li>Knowledge of privacy-preserving ML techniques and compliance requirements</li>\n<li>Contributing to open-source ML infrastructure projects</li>\n</ul>\n<p>We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_bd9625d9-99b","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4778843008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$320,000-$405,000 USD","x-skills-required":["Python","PyTorch","TensorFlow","JAX","Cloud platforms (AWS, GCP)","Container orchestration (Kubernetes)","Distributed systems principles","Data engineering tools (Spark, Airflow, streaming systems)"],"x-skills-preferred":["Large language models and modern transformer architectures","A/B testing frameworks and experimentation infrastructure for ML systems","Monitoring and alerting systems for ML model performance and data drift","Automated labeling systems and human-in-the-loop workflows","Trust & safety, fraud prevention, or content moderation domains","Privacy-preserving ML techniques and compliance requirements","Open-source ML infrastructure projects"],"datePosted":"2026-04-18T15:44:06.907Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, PyTorch, TensorFlow, JAX, Cloud platforms (AWS, GCP), Container orchestration (Kubernetes), Distributed systems principles, Data engineering tools (Spark, Airflow, streaming systems), Large language models and modern transformer architectures, A/B testing frameworks and experimentation infrastructure for ML systems, Monitoring and alerting systems for ML model performance and data drift, Automated labeling systems and human-in-the-loop workflows, Trust & safety, fraud prevention, or content moderation domains, Privacy-preserving ML techniques and compliance requirements, Open-source ML infrastructure projects","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":320000,"maxValue":405000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_5aacaad3-05b"},"title":"Senior Machine Learning Engineer, Payments","description":"<p>Job Title: Senior Machine Learning Engineer, Payments</p>\n<p>Location: Remote-USA</p>\n<p>The Payments team at Airbnb is responsible for everything related to settling money in Airbnb&#39;s global marketplace. As a Senior Machine Learning Engineer for Payments, you will be the catalyst that transforms bold AI innovation into production systems that make Airbnb Payment experience feel effortless and secure.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Spearhead LLM agents, real-time anomaly detectors, and other breakthrough solutions that solve real-world problems and create product magic.</li>\n</ul>\n<ul>\n<li>Collaborate with product, engineering, ops, and data science to spot high-leverage opportunities, refine AI/ML requirements, make principled architecture choices, and measure business value with clear, data-driven metrics.</li>\n</ul>\n<ul>\n<li>Design, train, deploy, and operate large-scale AI applications for both batch and streaming workloads, ensuring low latency, high reliability, and continuous improvement via automated monitoring and retraining loops.</li>\n</ul>\n<ul>\n<li>Mentor and inspire teammates, fostering a collaborative, experimentation-driven environment where cutting-edge research meets production excellence and every engineer is empowered to push AI boundaries at Airbnb.</li>\n</ul>\n<p>Your Expertise:</p>\n<ul>\n<li>5+ years of industry experience in applied AI/ML, inclusive MS or PhD in relevant fields.</li>\n</ul>\n<ul>\n<li>Strong programming (Python/Java) and data engineering skills.</li>\n</ul>\n<ul>\n<li>Proven mastery of modern AI/LLM workflows , prompt engineering, fine-tuning (LoRA, RLHF), hallucination mitigation, safety guardrails, and rigorous online/offline testing to minimize training/inference drift and ensure reliable outcomes.</li>\n</ul>\n<ul>\n<li>Hands-on experience with at least three of the following: PyTorch/TensorFlow, scalable inference stacks, vector search, orchestration/MLOps platforms (Kubeflow, Airflow), large-scale data streaming &amp; processing (Spark, Ray, Kafka).</li>\n</ul>\n<ul>\n<li>Demonstrated success designing, deploying, and monitoring production AI systems , e.g., personalization engines, generative content services , complete with drift/cost/latency monitoring, automated retraining triggers, and cross-functional collaboration that translates ambiguous business needs into measurable AI impact.</li>\n</ul>\n<ul>\n<li>Prior knowledge of AI/ML applications in the Payments domain is highly desirable.</li>\n</ul>\n<p>Our Commitment To Inclusion &amp; Belonging:</p>\n<p>Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively led people, and to develop the best products, services, and solutions.</p>\n<p>How We&#39;ll Take Care of You:</p>\n<p>Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs, and market demands. The base pay range is subject to change and may be modified in the future. 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This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems.\\n\\nResponsibilities:\\n\\n- Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability\\n- Debug and resolve complex issues across the full stack,from hardware errors and networking to training dynamics and evaluation infrastructure\\n- Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance\\n- Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams\\n- Build and maintain production logging, monitoring dashboards, and evaluation infrastructure\\n- Add new capabilities to the training codebase, such as long context support or novel architectures\\n- Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams\\n- Contribute to the team&#39;s institutional knowledge by documenting systems, debugging approaches, and lessons learned\\n\\nYou May Be a Good Fit If You:\\n\\n- Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems\\n- Genuinely enjoy both research and engineering work,you&#39;d describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other\\n- Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure\\n- Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs\\n- Excel at debugging complex, ambiguous problems across multiple layers of the stack\\n- Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents\\n- Are passionate about the work itself and want to refine your craft as a research engineer\\n- Care about the societal impacts of AI and responsible scaling\\n\\nStrong Candidates May Also Have:\\n\\n- Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale\\n- Contributed to open-source LLM frameworks (e.g., open_lm, llm-foundry, mesh-transformer-jax)\\n- Published research on model training, scaling laws, or ML systems\\n- Experience with production ML systems, observability tools, or evaluation infrastructure\\n- Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence\\n\\nWhat Makes This Role Unique:\\n\\nThis is not a typical research engineering role. The work is highly operational,you&#39;ll be deeply involved in keeping our production models training smoothly, which means being responsive to incidents, flexible about priorities, and comfortable with uncertainty. During launches, the team often works extended hours and may need to respond to issues on evenings and weekends.\\n\\nHowever, this operational intensity comes with extraordinary learning opportunities. You&#39;ll gain hands-on experience with some of the largest, most sophisticated training runs in the industry. You&#39;ll work alongside world-class researchers and engineers, and the institutional knowledge you build will compound in ways that can&#39;t be easily transferred. For people who thrive on this type of work, it&#39;s uniquely rewarding.\\n\\nWe&#39;re building a close-knit team of people who genuinely care about doing excellent work together. If you&#39;re someone who wants to be part of training the models that will define the future of AI,and you&#39;re excited about the full reality of what that entails,we&#39;d love to hear from you.\\n\\nLocation:\\n\\nThis role requires working in-office 5 days per week in London.\\n\\nDeadline to apply:\\n\\nNone. Applications will be reviewed on a rolling basis.\\n\\nThe annual compensation range for this role is listed below.\\n\\nFor sales roles, the range provided is the role’s On Target Earnings (&quot;OTE&quot;) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\\n\\nAnnual Salary:\\n\\n£260,000-£630,000 GBP\\n\\nLogistics\\n\\nMinimum education:\\n\\nBachelor’s degree or an equivalent combination of education, training, and/or experience\\n\\nRequired field of study:\\n\\nA field relevant to the role as demonstrated through coursework, training, or professional experience\\n\\nMinimum years of experience:\\n\\nYears of experience required will correlate with the internal job level requirements for the position\\n\\nLocation-based hybrid policy:\\n\\nCurrently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\\n\\nVisa sponsorship:\\n\\nWe do sponsor visas! However, we aren&#39;t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\\n\\nWe encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you&#39;re interested in this work. We think AI systems like the ones we&#39;re building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.\\n\\nYour safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you&#39;re ever unsure about a communication, don&#39;t click any links,visit anthropic.com/careers directly for confirmed position openings.\\n\\nHow we&#39;re different\\n\\nWe believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact , advancing our long-term goals of steerable, trustworthy AI , rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. 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This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems.\\n\\n## Responsibilities:\\n\\n- Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability\\n- Debug and resolve complex issues across the full stack,from hardware errors and networking to training dynamics and evaluation infrastructure\\n- Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance\\n- Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams\\n- Build and maintain production logging, monitoring dashboards, and evaluation infrastructure\\n- Add new capabilities to the training codebase, such as long context support or novel architectures\\n- Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams\\n- Contribute to the team&#39;s institutional knowledge by documenting systems, debugging approaches, and lessons learned\\n\\n## You May Be a Good Fit If You:\\n\\n- Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems\\n- Genuinely enjoy both research and engineering work,you&#39;d describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other\\n- Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure\\n- Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs\\n- Excel at debugging complex, ambiguous problems across multiple layers of the stack\\n- Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents\\n- Are passionate about the work itself and want to refine your craft as a research engineer\\n- Care about the societal impacts of AI and responsible scaling\\n\\n## Strong Candidates May Also Have:\\n\\n- Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale\\n- Contributed to open-source LLM frameworks (e.g., open_lm, llm-foundry, mesh-transformer-jax)\\n- Published research on model training, scaling laws, or ML systems\\n- Experience with production ML systems, observability tools, or evaluation infrastructure\\n- Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence\\n\\n## What Makes This Role Unique:\\n\\nThis is not a typical research engineering role. The work is highly operational,you&#39;ll be deeply involved in keeping our production models training smoothly, which means being responsive to incidents, flexible about priorities, and comfortable with uncertainty. During launches, the team often works extended hours and may need to respond to issues on evenings and weekends.\\n\\nHowever, this operational intensity comes with extraordinary learning opportunities. You&#39;ll gain hands-on experience with some of the largest, most sophisticated training runs in the industry. You&#39;ll work alongside world-class researchers and engineers, and the institutional knowledge you build will compound in ways that can&#39;t be easily transferred. For people who thrive on this type of work, it&#39;s uniquely rewarding.\\n\\nWe&#39;re building a close-knit team of people who genuinely care about doing excellent work together. If you&#39;re someone who wants to be part of training the models that will define the future of AI,and you&#39;re excited about the full reality of what that entails,we&#39;d love to hear from you.\\n\\nLocation: This role requires working in-office 5 days per week in San Francisco.\\n\\nDeadline to apply: None. Applications will be reviewed on a rolling basis.\\n\\nThe annual compensation range for this role is listed below.\\n\\nFor sales roles, the range provided is the role’s On Target Earnings (&quot;OTE&quot;) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\\n\\nAnnual Salary: $350,000-$850,000 USD\\n\\n## Logistics\\n\\nMinimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\\n\\nRequired field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience\\n\\nMinimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\\n\\nLocation-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\\n\\nVisa sponsorship: We do sponsor visas! However, we aren&#39;t able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\\n\\nWe encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you&#39;re interested in this work. We think AI systems like the ones we&#39;re building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.\\n\\nYour safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you&#39;re ever unsure about a communication, don&#39;t click any links,visit anthropic.com/careers directly for confirmed position openings.\\n\\n## How we&#39;re different\\n\\nWe believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact , advancing our long-term goals of steerable, trustworthy AI , rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. 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As a Research Engineer, you&#39;ll touch all parts of our code and infrastructure, whether that&#39;s making the cluster more reliable for our big jobs, improving throughput and efficiency, running and designing scientific experiments, or improving our dev tooling.</p>\n<p>You&#39;ll be working on large-scale ML systems from the ground up, making safe, steerable, trustworthy systems. You&#39;ll be excited to write code when you understand the research context and more broadly why it&#39;s important.</p>\n<p>Strong candidates may also have experience with high performance, large-scale ML systems, GPUs, Kubernetes, Pytorch, or OS internals, language modeling with transformers, reinforcement learning, and large-scale ETL.</p>\n<p>Representative projects may include optimizing the throughput of a new attention mechanism, comparing the compute efficiency of two Transformer variants, making a Wikipedia dataset in a format models can easily consume, scaling a distributed training job to thousands of GPUs, writing a design doc for fault tolerance strategies, and creating an interactive visualization of attention between tokens in a language model.</p>\n<p>The annual compensation range for this role is $350,000-$500,000 USD.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_279d67f2-5b5","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4951814008","x-work-arrangement":"hybrid","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":"$350,000-$500,000 USD","x-skills-required":["software engineering","machine learning","high performance computing","Kubernetes","Pytorch","OS internals","language modeling","reinforcement learning","large-scale ETL"],"x-skills-preferred":["GPU","transformers","distributed training"],"datePosted":"2026-04-18T15:42:28.391Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"New York City, NY; New York City, NY | Seattle, WA; San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"software engineering, machine learning, high performance computing, Kubernetes, Pytorch, OS internals, language modeling, reinforcement learning, large-scale ETL, GPU, transformers, distributed training","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":500000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_daa2eb47-aa0"},"title":"AI Engineer - FDE (Forward Deployed Engineer)","description":"<p>The AI Forward Deployed Engineering (AI FDE) team at Databricks is a highly specialized customer-facing AI team. We deliver professional services engagements to help our customers build and productionize first-of-its-kind AI applications. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations, as well as support internal subject matter expert (SME) teams.</p>\n<p>We view our team as an ensemble: we look for individuals with strong, unique specializations to improve the overall strength of the team. This team is the right fit for you if you love working with customers, teammates, and fueling your curiosity for the latest trends in GenAI, LLMOps, and ML more broadly. Open to remote locations.</p>\n<p>The impact you will have:</p>\n<ul>\n<li>Develop cutting-edge GenAI solutions, incorporating the latest techniques from our Mosaic AI Research to solve customer problems</li>\n<li>Own production rollouts of consumer and internally facing GenAI applications</li>\n<li>Serve as a trusted technical advisor to customers across a variety of domains</li>\n<li>Present at conferences such as Data + AI Summit, recognized as a thought leader internally and externally</li>\n<li>Collaborate cross-functionally with the product and engineering teams to influence priorities and shape the product roadmap</li>\n</ul>\n<p>What we look for:</p>\n<ul>\n<li>Experience building GenAI applications, including RAG, multi-agent systems, Text2SQL, fine-tuning, etc., with tools such as HuggingFace, LangChain, and DSPy</li>\n<li>Expertise in deploying production-grade GenAI applications, including evaluation and optimizations</li>\n<li>Extensive years of hands-on industry data science experience, leveraging common machine learning and data science tools, i.e. pandas, scikit-learn, PyTorch, etc.</li>\n<li>Experience building production-grade machine learning deployments on AWS, Azure, or GCP</li>\n<li>Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience</li>\n<li>Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike</li>\n<li>Passion for collaboration, life-long learning, and driving business value through AI</li>\n</ul>\n<p>Benefits:</p>\n<p>At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region, please visit our website.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_daa2eb47-aa0","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Databricks","sameAs":"https://databricks.com/","logo":"https://logos.yubhub.co/databricks.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/databricks/jobs/8330188002","x-work-arrangement":"remote","x-experience-level":null,"x-job-type":"full-time","x-salary-range":null,"x-skills-required":["GenAI","HuggingFace","LangChain","DSPy","pandas","scikit-learn","PyTorch","AWS","Azure","GCP"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:42:25.160Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Melbourne, Australia"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"GenAI, HuggingFace, LangChain, DSPy, pandas, scikit-learn, PyTorch, AWS, Azure, GCP"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_1507524b-770"},"title":"Research Engineer, Performance RL","description":"<p>We&#39;re hiring a Research Engineer to join our Code RL team within the RL organization. As a Research Engineer, you&#39;ll advance our models&#39; ability to safely write correct, fast code for accelerators.</p>\n<p>You&#39;ll need to know accelerator performance well to turn it into tasks and signals models can learn from. Specifically, you will:</p>\n<ul>\n<li>Invent, design and implement RL environments and evaluations.</li>\n<li>Conduct experiments and shape our research roadmap.</li>\n<li>Deliver your work into training runs.</li>\n<li>Collaborate with other researchers, engineers, and performance engineering specialists across and outside Anthropic.</li>\n</ul>\n<p>You may be a good fit if you:</p>\n<ul>\n<li>Have expertise with accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch).</li>\n<li>Have worked across the stack – kernels, model code, distributed systems.</li>\n<li>Know how to balance research exploration with engineering implementation.</li>\n<li>Are passionate about AI&#39;s potential and committed to developing safe and beneficial systems.</li>\n</ul>\n<p>Strong candidates may also have:</p>\n<ul>\n<li>Experience with reinforcement learning.</li>\n<li>Experience porting ML workloads between different types of accelerators.</li>\n<li>Familiarity with LLM training methodologies.</li>\n</ul>\n<p>The annual compensation range for this role is $350,000-$850,000 USD.</p>\n<p>We&#39;re an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.</p>\n<p>We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact , advancing our long-term goals of steerable, trustworthy AI , rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science.</p>\n<p>We kitchen is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_1507524b-770","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/5160330008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$350,000-$850,000 USD","x-skills-required":["accelerators","ML framework programming","distributed systems","reinforcement learning","LLM training methodologies"],"x-skills-preferred":["CUDA","ROCm","Triton","Pallas","JAX","PyTorch"],"datePosted":"2026-04-18T15:42:09.925Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"accelerators, ML framework programming, distributed systems, reinforcement learning, LLM training methodologies, CUDA, ROCm, Triton, Pallas, JAX, PyTorch","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":850000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_37a6bec1-c5f"},"title":"Privacy Research Engineer, Safeguards","description":"<p>We are looking for researchers to help mitigate the risks that come with building AI systems. 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In this role, you&#39;ll design and implement privacy-preserving techniques, audit our current techniques, and set the direction for how Anthropic handles privacy more broadly.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Lead our privacy analysis of frontier models, carefully auditing the use of data and ensuring safety throughout the process</li>\n<li>Develop privacy-first training algorithms and techniques</li>\n<li>Develop evaluation and auditing techniques to measure the privacy of training algorithms</li>\n<li>Advocate on behalf of our users to ensure responsible handling of all data</li>\n</ul>\n<p>You may be a good fit if you have:</p>\n<ul>\n<li>Experience working on privacy-preserving machine learning</li>\n<li>A track record of shipping products and features inside a fast-moving environment</li>\n<li>Strong coding skills in Python and familiarity with ML frameworks like PyTorch or JAX.</li>\n<li>Deep familiarity with large language models, how they work, and how they are trained</li>\n<li>Have experience working with privacy-preserving techniques (e.g., differential privacy and how it is different from k-anonymity, l-diversity, and t-closeness)</li>\n<li>Experience supporting fast-paced startup engineering teams</li>\n<li>Demonstrated success in bringing clarity and ownership to ambiguous technical problems</li>\n<li>Proven ability to lead cross-functional security initiatives and navigate complex organizational dynamics</li>\n</ul>\n<p>Strong candidates may also:</p>\n<ul>\n<li>Have published papers on the topic of privacy-preserving ML at top academic venues</li>\n<li>Prior experience training large language models (e.g., collecting training datasets, pre-training models, post-training models via fine-tuning and RL, running evaluations on trained models)</li>\n<li>Prior experience developing tooling to support privacy-preserving ML (e.g., differential privacy in TF-Privacy or Opacus)</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_37a6bec1-c5f","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Anthropic","sameAs":"https://www.anthropic.com/","logo":"https://logos.yubhub.co/anthropic.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/anthropic/jobs/4949108008","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$320,000-$485,000 USD","x-skills-required":["Python","PyTorch","JAX","Machine Learning","Differential Privacy","K-Anonymity","L-Diversity","T-Closeness"],"x-skills-preferred":["Large Language Models","Fast-Paced Startup Engineering Teams","Cross-Functional Security Initiatives"],"datePosted":"2026-04-18T15:42:09.915Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, PyTorch, JAX, Machine Learning, Differential Privacy, K-Anonymity, L-Diversity, T-Closeness, Large Language Models, Fast-Paced Startup Engineering Teams, Cross-Functional Security Initiatives","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":320000,"maxValue":485000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_bb7aa015-076"},"title":"AI Engineer - FDE (Forward Deployed Engineer)","description":"<p>We are seeking an AI Engineer - FDE (Forward Deployed Engineer) to join our team. As an AI Engineer, you will develop cutting-edge GenAI solutions, incorporating the latest techniques from our Mosaic AI Research to solve customer problems. You will own production rollouts of consumer and internally facing GenAI applications, serve as a trusted technical advisor to customers across a variety of domains, and present at conferences such as Data + AI Summit, recognized as a thought leader internally and externally. You will collaborate cross-functionally with the product and engineering teams to influence priorities and shape the product roadmap.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Develop cutting-edge GenAI solutions, incorporating the latest techniques from our Mosaic AI Research to solve customer problems</li>\n<li>Own production rollouts of consumer and internally facing GenAI applications</li>\n<li>Serve as a trusted technical advisor to customers across a variety of domains</li>\n<li>Present at conferences such as Data + AI Summit, recognized as a thought leader internally and externally</li>\n<li>Collaborate cross-functionally with the product and engineering teams to influence priorities and shape the product roadmap</li>\n</ul>\n<p>Requirements:</p>\n<ul>\n<li>Experience building GenAI applications, including RAG, multi-agent systems, Text2SQL, fine-tuning, etc., with tools such as HuggingFace, LangChain, and DSPy</li>\n<li>Expertise in deploying production-grade GenAI applications, including evaluation and optimizations</li>\n<li>Extensive years of hands-on industry data science experience, leveraging common machine learning and data science tools, i.e. pandas, scikit-learn, PyTorch, etc.</li>\n<li>Experience building production-grade machine learning deployments on AWS, Azure, or GCP</li>\n<li>Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience</li>\n<li>Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike</li>\n<li>Passion for collaboration, life-long learning, and driving business value through AI</li>\n</ul>\n<p>Benefits: At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. 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Strong candidates should have familiarity with elements of language model training, evaluation, and inference and eagerness to quickly dive and get up to speed in areas they are not yet an expert on.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Design and implement large-scale infrastructure systems to support AI scientist training, evaluation, and deployment across distributed environments</li>\n<li>Identify and resolve infrastructure bottlenecks impeding progress toward scientific capabilities</li>\n<li>Develop robust and reliable evaluation frameworks for measuring progress towards scientific AGI</li>\n<li>Build scalable and performant VM/sandboxing/container architectures to safely execute long-horizon AI tasks and scientific workflows</li>\n<li>Collaborate to translate experimental requirements into production-ready infrastructure</li>\n<li>Develop large scale data pipelines to handle advanced language model training requirements</li>\n<li>Optimize large scale training and inference pipelines for stable and efficient reinforcement learning</li>\n</ul>\n<p>You may be a good fit if you:</p>\n<ul>\n<li>Have 6+ years of highly-relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems</li>\n<li>Are a strong communicator and enjoy working collaboratively</li>\n<li>Possess deep knowledge of performance optimization techniques and system architectures for high-throughput ML workloads</li>\n<li>Have experience with containerization technologies (Docker, Kubernetes) and orchestration at scale</li>\n<li>Have proven track record of building large-scale data pipelines and distributed storage systems</li>\n<li>Excel at diagnosing and resolving complex infrastructure challenges in production environments</li>\n<li>Can work effectively across the full ML stack from data pipelines to performance optimization</li>\n<li>Have experience collaborating with other researchers to scale experimental ideas</li>\n<li>Thrive in 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…)"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:41:42.408Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"large-scale distributed systems, containerization technologies (Docker, Kubernetes), performance optimization techniques, system architectures for high-throughput ML workloads, data pipelines, distributed storage systems, ML frameworks (PyTorch, JAX, etc.), GPU/TPU architectures, cloud platforms (AWS, GCP), VM and container orchestration, workflow orchestration tools, experiment management systems, reinforcement learning, large scale data pipelines (Beam, Spark, Dask, …)","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":350000,"maxValue":850000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_812f2abf-252"},"title":"PhD GenAI Research Scientist Intern","description":"<p>At Databricks, we are realities obsessed with enabling data teams to solve the world&#39;s toughest problems, from security threat detection to cancer drug development. We do this by building and running the world&#39;s best data and AI platform, so our customers can focus on the high-value challenges that are central to their own missions.</p>\n<p>The Mosaic AI organization enables companies to develop AI models and systems using their own data, with technologies ranging from fine-tuning LLMs for enterprise domains, to a platform for building compound AI systems that use retrieval and agents. Mosaic AI is committed to the belief that a company&#39;s AI models are just as valuable as any other core IP, and that high-quality AI models should be available to all.</p>\n<p>Job description:</p>\n<p>Most of the world&#39;s data+AI problems lie in enterprise domains, behind closed doors. Our research team&#39;s goal is to push the frontier of &#39;domain adaptation&#39; - how can we develop LLMs and AI systems that work well for custom domains. To do this we are tackling open research problems on a range of topics, from how to scale/automate eval, fine tune with synthetic data, retrieval augmentation, fast/efficient inference and more.</p>\n<p>You will work with our research team on projects focused on adapting LLMs and AI systems towards enterprise domains. This may include:</p>\n<ul>\n<li>Adapting, improving, and evaluating a method from the literature.</li>\n<li>Designing an entirely new method for domain adaptation.</li>\n<li>Composing together multiple methods to create new recipes for efficient post-training.</li>\n<li>Evaluation of LLMs and AI systems.</li>\n</ul>\n<p>Your qualifications and qualities:</p>\n<ul>\n<li>Required:</li>\n<li>Research experience in and proficiency with the fundamentals of deep learning.</li>\n<li>Pursuing a PhD in computer science or related fields (electrical engineering, neuroscience, physics, math, etc.).</li>\n<li>Proficient software engineering skills, including with PyTorch.</li>\n</ul>\n<p>Pay Range Transparency</p>\n<p>Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. 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The individual will closely partner with Product &amp; Engineering teams to execute the roadmap for Twilio&#39;s AI/ML products and services.</p>\n<p>You will understand customers&#39; needs, build data products that work at a global scale, and own end-to-end execution of large-scale ML solutions.</p>\n<p>To thrive in this role, you must have a deep background in ML engineering and a consistent track record of solving data &amp; machine-learning problems at scale.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Build and maintain scalable machine learning solutions in production</li>\n<li>Train and validate both deep learning-based and statistical-based models considering use-case, complexity, performance, and robustness</li>\n<li>Demonstrate end-to-end understanding of applications and develop a deep understanding of the &#39;why&#39; behind our models &amp; systems</li>\n<li>Partner with product managers, tech leads, and stakeholders to analyze business problems, clarify requirements, and define the scope of the systems needed</li>\n<li>Work closely with data platform teams to build robust scalable batch and real-time data pipelines</li>\n<li>Collaborate with software engineers, build tools to enhance productivity, and to ship and maintain ML models</li>\n<li>Drive high engineering standards on the team through mentoring and knowledge sharing</li>\n<li>Uphold engineering best practices around code reviews, automated testing, and monitoring</li>\n</ul>\n<p>Qualifications:</p>\n<ul>\n<li>7+ years of applied ML experience with proficiency in Python</li>\n<li>Strong background in the foundations of Machine Learning and building blocks of modern Deep Learning</li>\n<li>Track record of building, shipping, and maintaining Machine Learning models in production in an ambiguous and fast-paced environment</li>\n<li>Track record of designing and architecting large-scale experiments and analysis to inform product roadmap</li>\n<li>Familiarity with ML Ops concepts related to testing and maintaining models in production such as testing, retraining, and monitoring</li>\n<li>Demonstrated ability to ramp up, understand, and operate effectively in new application/business domains</li>\n<li>Experience working in an agile team environment with changing priorities</li>\n<li>Experience of working on AWS</li>\n</ul>\n<p>Desired:</p>\n<ul>\n<li>Experience with Large Language Models</li>\n</ul>\n<p>Travel:</p>\n<p>We prioritize connection and opportunities to build relationships with our customers and each other. 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You will maximize GPU utilization and performance at unprecedented scale, develop cutting-edge optimizations that directly enable new model capabilities, and dramatically improve inference efficiency.</p>\n<p>Working at the intersection of hardware and software, you will implement state-of-the-art techniques from custom kernel development to distributed system architectures. 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business impact.</p>\n<p>Key responsibilities:</p>\n<ul>\n<li>Harness frontier models to drive real-world high-impact outcomes</li>\n</ul>\n<ul>\n<li>Build evaluations, training data, and infrastructure to support AI deployments and rapid iterations</li>\n</ul>\n<ul>\n<li>Collaborate with researchers and product managers to translate research advancements into tangible product features.</li>\n</ul>\n<ul>\n<li>Contribute to the development of best practices for building and deploying generative AI applications.</li>\n</ul>\n<ul>\n<li>Contribute signal to influence the development of frontier models</li>\n</ul>\n<ul>\n<li>Lead the architecture and development of new products &amp; features from 0 to 1.</li>\n</ul>\n<p>About you:</p>\n<p>In order to set you up for success as a Staff Research Engineer, Applied AI at Google DeepMind, we look for the following skills and experience:</p>\n<p>Required Skills:</p>\n<ul>\n<li>Bachelor&#39;s degree or equivalent practical experience.</li>\n</ul>\n<ul>\n<li>8 years of experience in software development, and with data structures/algorithms.</li>\n</ul>\n<ul>\n<li>5 years of hands-on experience in AI research (e.g. RL, finetuning, evals), AI applications, or model deployment</li>\n</ul>\n<ul>\n<li>Proven experience in rapidly developing and shipping software products.</li>\n</ul>\n<ul>\n<li>Deep understanding of software development best practices, including testing &amp; 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You’ll have the opportunity to shape the future of AI-driven products at Databricks, work with cutting-edge models, and collaborate with a world-class team of AI and ML experts.</p>\n<p>Pay Range Transparency</p>\n<p>Databricks is committed to fair and equitable compensation practices. The pay range for this role is $190,000-$285,000 USD.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_271bbfdd-0a2","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Databricks","sameAs":"https://databricks.com","logo":"https://logos.yubhub.co/databricks.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/databricks/jobs/8401114002","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$190,000-$285,000 USD","x-skills-required":["Python","TensorFlow","PyTorch","Scalable ML architectures","Language modeling technologies","Machine learning engineering"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:39:49.661Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, California"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, TensorFlow, PyTorch, Scalable ML architectures, Language modeling technologies, Machine learning engineering","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":190000,"maxValue":285000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_aa7ebb20-cd1"},"title":"Research Engineer, Post-Training for Code Security Analysis","description":"<p>JOB DESCRIPTION:</p>\n<p>About Us</p>\n<p>Artificial Intelligence could be one of humanity’s most useful inventions. 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reasoning services across text, voice, and video data modalities to support model training and evaluation.</p>\n<p>The role may include recording audio responses, participating in video-based tasks, or producing step-by-step quantitative reasoning traces , all of which are essential job functions required to fulfill xAI’s mission.</p>\n<p>All outputs are considered work-for-hire and owned by xAI.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_158e7279-b99","directApply":true,"hiringOrganization":{"@type":"Organization","name":"xAI","sameAs":"https://www.xai.com/","logo":"https://logos.yubhub.co/xai.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/xai/jobs/5040333007","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time|contract","x-salary-range":"$45/hour - $100/hour","x-skills-required":["Master’s or PhD in a strongly 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systems.</li>\n<li>Ability to deliver optimal end-to-end user experiences.</li>\n<li>Hands-on contributor with initiative, excellence, strong work ethic, prioritization skills, and excellent communication.</li>\n</ul>\n<p>Preferred Skills and Experience:</p>\n<ul>\n<li>Experience in SFT, RL, evals, human/synthetic data collection, or agentic systems.</li>\n<li>Proficiency in Python, JAX/XLA, PyTorch, Rust/C++, Spark, Ray, and related large-scale frameworks.</li>\n<li>Domain expertise in multimodal applications such as graphics engines, rendering techniques, image/video understanding and generation, world models, real-time simulation, or controllable/long-horizon visual content creation (audio/speech processing or music/audio generation experience is a plus where it supports video).</li>\n<li>Experience with agentic RL training, controllable/long-horizon generation, or multimodal agents that reason and act across modalities (especially in visual domains).</li>\n</ul>\n<p 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Advance understanding and generation across modalities,image, video, audio, and text,spanning the full stack: data curation/acquisition, tokenizer training, large-scale pre-training, post-training/alignment, infrastructure/scaling, evaluation, tooling/demos, and end-to-end product experiences.</p>\n<p>Collaborate cross-functionally with pre-training, post-training, reasoning, data, applied, and product teams to deliver frontier capabilities in multimodal reasoning, world modeling, tool use, agentic behaviors, and interactive human-AI collaboration. Contribute to building models that can see, hear, reason about, and interact with the world in real time at unprecedented levels.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Design, build, and optimize large-scale distributed systems for multimodal pre-training, post-training, inference, data processing, and tokenization at web/petabyte scale.</li>\n<li>Develop high-throughput pipelines for data acquisition, preprocessing, filtering, generation, decoding, loading, crawling, visualization, and management (images, videos, audio + text).</li>\n<li>Advance multimodal capabilities including spatial-temporal compression, cross-modal alignment, world modeling, reasoning, emergent abilities, audio/image/video understanding &amp; generation, real-time video processing, and noisy data handling.</li>\n<li>Drive data quality and studies: curation (human/synthetic), filtering techniques, analysis, and scalable pipelines to support trillion-parameter models.</li>\n<li>Create evaluation frameworks, internal benchmarks, reward models, and metrics that capture real-world usage, failure modes, interactive dynamics, and human-AI synergy.</li>\n<li>Innovate on algorithms, modeling approaches, hardware/software/algorithm co-design, and scaling paradigms for state-of-the-art performance.</li>\n<li>Build research tooling, user-friendly interfaces, prototypes/demos, full-stack applications, and enable rapid iteration based on feedback.</li>\n<li>Work across the stack (pre-training → SFT/RL/post-training) to enable reasoning, tool calling, agentic behaviors, orchestration, and seamless real-time interactions.</li>\n</ul>\n<p><strong>Basic Qualifications</strong></p>\n<ul>\n<li>Hands-on experience with multimodal pre-training, post-training, or fine-tuning (vision, audio, video, or cross-modal).</li>\n<li>Expert-level proficiency in Python (core language), with strong experience in at least one of: JAX / PyTorch / XLA.</li>\n<li>Proven track record building or optimizing large-scale distributed ML systems (training/inference optimization, GPU utilization, multi-GPU/TPU setups, hardware co-design).</li>\n<li>Deep experience designing and running data pipelines at scale: curation, filtering, generation, quality studies, especially for noisy/real-world multimodal data.</li>\n<li>Strong fundamentals in evaluation design, benchmarks, reward modeling, or RL techniques (particularly for interactive/agentic behaviors).</li>\n<li>Proactive self-starter who thrives in high-intensity environments and is passionate about pushing multimodal AI frontiers.</li>\n<li>Willingness to own end-to-end initiatives and do whatever it takes to deliver breakthrough user experiences.</li>\n</ul>\n<p><strong>Preferred Skills and Experience</strong></p>\n<ul>\n<li>Experience leading major improvements in model capabilities through better data, modeling, algorithms, or scaling.</li>\n<li>Familiarity with state-of-the-art in multimodal LLMs, scaling laws, tokenizers, compression techniques, reasoning, or agentic systems.</li>\n<li>Proficiency in Rust and/or C++ for performance-critical components.</li>\n<li>Hands-on work with large-scale orchestration tools such as Spark, Ray, or Kubernetes.</li>\n<li>Background building full-stack tooling: performant interfaces, real-time research demos/apps, or end-to-end product ownership.</li>\n<li>Passion for end-to-end user experience in interactive, real-time multimodal AI systems.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_540ce49c-271","directApply":true,"hiringOrganization":{"@type":"Organization","name":"xAI","sameAs":"https://www.xai.com","logo":"https://logos.yubhub.co/xai.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/xai/jobs/5111374007","x-work-arrangement":"onsite","x-experience-level":"staff","x-job-type":"full-time","x-salary-range":"$180,000 - 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This role is focused on modern reconstruction techniques,both feed-forward and optimization-based,with an emphasis on novel representations, robust optimization, and scalable training and inference pipelines.</p>\n<p>This is a hands-on, research-driven role for someone who enjoys working at the intersection of computer vision, graphics, and machine learning. You&#39;ll collaborate closely with research scientists, ML engineers, and product teams to translate cutting-edge reconstruction ideas into production-ready systems that power core product capabilities.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Design and implement modern 3D reconstruction systems, including feed-forward and optimization-based approaches for geometry, appearance, and scene understanding.</li>\n<li>Research, prototype, and productionize advanced 3D representations (e.g., implicit functions, point-based or volumetric methods, hybrid representations) with a focus on accuracy, efficiency, and scalability.</li>\n<li>Develop and improve optimization pipelines for multi-view reconstruction, including camera pose estimation, joint geometry/appearance optimization, and robust loss formulations.</li>\n<li>Build end-to-end training and evaluation workflows for 3D reconstruction models, from data preparation and supervision strategies to large-scale experiments and metrics.</li>\n<li>Collaborate with data and infrastructure teams to ensure reconstruction methods integrate cleanly with existing 3D data pipelines, rendering systems, and downstream applications.</li>\n<li>Analyze failure modes and data quality issues in real-world reconstruction scenarios, and design principled solutions to improve robustness and generalization.</li>\n<li>Optimize performance across the stack, including memory usage, training speed, and inference latency, to support large-scale datasets and production constraints.</li>\n<li>Contribute to technical direction by proposing new research ideas, mentoring teammates, and helping set best practices for 3D reconstruction across the organization.</li>\n</ul>\n<p><strong>Key Qualifications:</strong></p>\n<ul>\n<li>6+ years of experience working on 3D reconstruction, multi-view geometry, or related areas in computer vision, graphics, or machine learning.</li>\n<li>Strong foundation in modern 3D reconstruction techniques, including feed-forward neural methods or optimization-based approaches.</li>\n<li>Deep experience with 3D representations and their tradeoffs (e.g., implicit fields, point-based methods, meshes, volumes) or with large-scale optimization pipelines for reconstruction.</li>\n<li>Proficiency in Python and/or C++, with hands-on experience building research or production systems.</li>\n<li>Experience with deep learning frameworks (e.g., PyTorch) and numerical optimization tools.</li>\n<li>Familiarity with rendering, differentiable rendering, or graphics pipelines, and how they interact with reconstruction systems.</li>\n<li>Proven ability to work in ambiguous, fast-moving environments and drive projects from concept through deployment.</li>\n<li>A strong sense of ownership and scientific rigor: you care deeply about correctness, reproducibility, and measurable improvements.</li>\n<li>Enjoy collaborating with a small, high-caliber team and raising the technical bar through thoughtful design, experimentation, and code quality.</li>\n</ul>\n<p><strong>Who You Are:</strong></p>\n<ul>\n<li>Fearless Innovator: We need people who thrive on challenges and aren&#39;t afraid to tackle the impossible.</li>\n<li>Resilient Builder: Impacting Large World Models isn&#39;t a sprint; it&#39;s a marathon with hurdles. We&#39;re looking for builders who can weather the storms of groundbreaking research and come out stronger.</li>\n<li>Mission-Driven Mindset: Everything we do is in service of creating the best spatially intelligent AI systems, and using them to empower people.</li>\n<li>Collaborative Spirit: We&#39;re building something bigger than any one person. We need team players who can harness the power of collective intelligence.</li>\n</ul>\n<p>We&#39;re hiring the brightest minds from around the globe to bring diverse perspectives to our cutting-edge work. If you&#39;re ready to work on technology that will reshape how machines perceive and interact with the world, World Labs is your launchpad.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_19ef76c6-d81","directApply":true,"hiringOrganization":{"@type":"Organization","name":"World Labs","sameAs":"https://worldlabs.ai","logo":"https://logos.yubhub.co/worldlabs.ai.png"},"x-apply-url":"https://job-boards.greenhouse.io/worldlabs/jobs/4113005009","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$250,000-$350,000 base salary (good-faith estimate for San Francisco Bay Area upon hire; actual offer based on experience, skills, and qualifications)","x-skills-required":["3D reconstruction","multi-view geometry","computer vision","graphics","machine learning","Python","C++","PyTorch","numerical optimization tools","rendering","differentiable rendering","graphics pipelines"],"x-skills-preferred":[],"datePosted":"2026-04-17T13:10:17.060Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"3D reconstruction, multi-view geometry, computer vision, graphics, machine learning, Python, C++, PyTorch, numerical optimization tools, rendering, differentiable rendering, graphics pipelines","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":250000,"maxValue":350000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_2e513a92-ec5"},"title":"Research Scientist (Generative Modeling)","description":"<p>We are seeking a talented Research Scientist with a strong background in generative modeling, particularly diffusion models, to join our modeling team. This role is ideal for candidates with deep expertise in diffusion models applied to images, videos, or 3D assets and scenes.</p>\n<p>While experience in one or more of the following areas is a strong plus: large-scale model training, research in 3D computer vision.</p>\n<p>You will collaborate closely with researchers, engineers, and product teams to bring advanced 3D modeling and machine learning techniques into real-world applications, ensuring that our technology remains at the forefront of visual innovation. This role involves significant hands-on research and engineering work, driving projects from conceptualization through to production deployment.</p>\n<p>Key responsibilities include designing, implementing, and training large-scale diffusion models for generating 3D worlds, developing and experimenting with large-scale diffusion models to add novel control signals, adapting to target aesthetic preferences, or distilling for efficient inference, collaborating closely with research and product teams to understand and translate product requirements into effective technical roadmaps, contributing hands-on to all stages of model development including data curation, experimentation, evaluation, and deployment, continuously exploring and integrating cutting-edge research in diffusion and generative AI more broadly, acting as a key technical resource within the team, mentoring colleagues, and driving best practices in generative modeling and ML engineering.</p>\n<p>Ideal candidate profile includes 3+ years of experience in generative modeling or applied ML roles, extensive experience with machine learning frameworks such as PyTorch or TensorFlow, especially in the context of diffusion models and other generative models, deep expertise in at least one area of generative modeling, strong history of publications or open-source contributions involving large-scale diffusion models, strong coding proficiency in Python and experience with GPU-accelerated computing, ability to engage effectively with researchers and cross-functional teams, clearly translating complex technical ideas into actionable tasks and outcomes, comfortable operating within a dynamic startup environment with high levels of ambiguity, ownership, and innovation.</p>\n<p>Nice to have includes contributions to open-source projects in the fields of computer vision, graphics, or ML, familiarity with large-scale training infrastructure, experience integrating machine learning models into production environments, led or been involved with the development or training of large-scale, state-of-the-art generative models.</p>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_2e513a92-ec5","directApply":true,"hiringOrganization":{"@type":"Organization","name":"World Labs","sameAs":"https://worldlabs.ai","logo":"https://logos.yubhub.co/worldlabs.ai.png"},"x-apply-url":"https://job-boards.greenhouse.io/worldlabs/jobs/4089324009","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$250,000 - $325,000 base salary (good-faith estimate for San Francisco Bay Area upon hire; actual offer based on experience, skills, and qualifications)","x-skills-required":["generative modeling","diffusion models","PyTorch","TensorFlow","machine learning frameworks","large-scale model training","research in 3D computer vision","data curation","experimentation","evaluation","deployment","GPU-accelerated computing","Python"],"x-skills-preferred":["open-source contributions","large-scale training infrastructure","integrating machine learning models into production environments","leading or being involved with the development or training of large-scale, state-of-the-art generative models"],"datePosted":"2026-04-17T13:09:56.134Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"generative modeling, diffusion models, PyTorch, TensorFlow, machine learning frameworks, large-scale model training, research in 3D computer vision, data curation, experimentation, evaluation, deployment, GPU-accelerated computing, Python, open-source contributions, large-scale training infrastructure, integrating machine learning models into production environments, leading or being involved with the development or training of large-scale, state-of-the-art generative models","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":250000,"maxValue":325000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_1044b51e-cc6"},"title":"Senior Manager, Software - Perception","description":"<p>This position is ideal for an individual who thrives on building advanced perception systems that enable autonomous aircraft to operate effectively in complex and contested environments.</p>\n<p>A successful candidate will be skilled in developing real-time object detection, sensor fusion, and state estimation algorithms using data from diverse mission sensors such as EO/IR cameras, radars, and IMUs. The role requires strong algorithmic thinking, deep familiarity with airborne sensing systems, and the ability to deliver performant software in simulation and real-world conditions.</p>\n<p>Shield AI is committed to developing cutting-edge autonomy for unmanned aircraft operating across all Department of Defense (DoD) domains, including air, sea, and land. Our Perception Engineers are instrumental in creating the situational awareness that underpins autonomy, ensuring our systems understand and respond to the operational environment with speed, precision, and resilience.</p>\n<p><strong>Responsibilities:</strong></p>\n<ul>\n<li>Lead teams across autonomy, integration, and testing by aligning technical efforts, resolving cross-functional challenges, and driving mission-focused execution.</li>\n<li>Develop advanced perception algorithms for object detection, classification, and multi-target tracking across diverse sensor modalities.</li>\n<li>Implement sensor fusion frameworks by integrating data from vision systems, radars, and other mission sensors using probabilistic and deterministic fusion techniques.</li>\n<li>Develop state estimation capabilities by designing and refining algorithms for localization and pose estimation using IMU, GPS, vision, and other onboard sensing inputs.</li>\n<li>Analyze and utilize sensor ICDs to ensure correct data handling, interpretation, and synchronization.</li>\n<li>Optimize perception performance by tuning and evaluating perception pipelines for performance, robustness, and real-time efficiency in both simulation and real-world environments.</li>\n<li>Support autonomy integration by working closely with autonomy, systems, and integration teams to interface perception outputs with planning, behaviors, and decision-making modules.</li>\n<li>Validate in simulated and operational settings by leveraging synthetic data, simulation environments, and field testing to validate algorithm accuracy and mission readiness.</li>\n<li>Collaborate with hardware and sensor teams to ensure seamless integration of perception algorithms with onboard compute platforms and diverse sensor payloads.</li>\n<li>Drive innovation in airborne sensing by contributing novel ideas and state-of-the-art techniques to advance real-time perception capabilities for unmanned aircraft operating in complex, GPS-denied, or contested environments.</li>\n<li>Travel Requirement – Members of this team typically travel around 10-15% of the year (to different office locations, customer sites, and flight integration events).</li>\n</ul>\n<p><strong>Requirements:</strong></p>\n<ul>\n<li>BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, and/or similar degree, or equivalent practical experience.</li>\n<li>Typically requires a minimum of 10 years of related experience with a Bachelor’s degree; or 9 years and a Master’s degree; or 7 years with a PhD; or equivalent work experience.</li>\n<li>7+ years of experience in Unmanned Systems programs in the DoD or applied R&amp;D.</li>\n<li>2+ years of people leadership experience.</li>\n<li>Background in implementing algorithms such as Kalman Filters, multi-target tracking, or deep learning-based detection models.</li>\n<li>Familiarity with fusing data from radar, EO/IR cameras, or other sensors using probabilistic or rule-based approaches.</li>\n<li>Familiarity with SLAM, visual-inertial odometry, or sensor-fused localization approaches in real-time applications.</li>\n<li>Ability to interpret and work with Interface Control Documents (ICDs) and hardware integration specs.</li>\n<li>Proficiency with version control, debugging, and test-driven development in cross-functional teams.</li>\n<li>Ability to obtain a SECRET clearance.</li>\n</ul>\n<p><strong>Preferences:</strong></p>\n<ul>\n<li>Hands-on integration or algorithm development with airborne sensing systems.</li>\n<li>Experience with ML frameworks such as PyTorch or Tensorflow, particularly for vision-based object detection or classification tasks.</li>\n<li>Experience deploying perception software on SWaP-constrained platforms.</li>\n<li>Familiarity with validating perception systems during flight test events or operational environments.</li>\n<li>Understanding of sensing challenges in denied or degraded conditions.</li>\n<li>Exposure to perception applications across air, maritime, and ground platforms.</li>\n</ul>\n<p style=\"margin-top:24px;font-size:13px;color:#666;\">XML job scraping automation by <a href=\"https://yubhub.co\">YubHub</a></p>","url":"https://yubhub.co/jobs/job_1044b51e-cc6","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Shield AI","sameAs":"https://www.shield.ai","logo":"https://logos.yubhub.co/shield.ai.png"},"x-apply-url":"https://jobs.lever.co/shieldai/cebc0dd3-ffbf-4013-a2ad-ae32732cabd3","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$229,233 - $343,849 a year","x-skills-required":["BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, and/or similar degree","10+ years of related experience","7+ years of experience in Unmanned Systems programs in the DoD or applied R&D","2+ years of people leadership experience","Background in 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environments","Understanding of sensing challenges in denied or degraded conditions","Exposure to perception applications across air, maritime, and ground platforms"],"datePosted":"2026-04-17T13:04:16.670Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Washington, DC / San Diego, California / Boston, MA"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, and/or similar degree, 10+ years of related experience, 7+ years of experience in Unmanned Systems programs in the DoD or applied R&D, 2+ years of people leadership experience, Background in implementing algorithms such as Kalman Filters, multi-target tracking, or deep learning-based detection models, Familiarity with fusing data from radar, EO/IR cameras, or other sensors using probabilistic or rule-based approaches, Familiarity with SLAM, 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to lead technical teams and support direct projects integrating perception solutions for defense platforms.</p>\n<p>Shield AI is committed to developing cutting-edge autonomy for unmanned aircraft operating across all Department of Defense (DoD) domains, including air, sea, and land.\nOur Perception Engineers are instrumental in creating the situational awareness that underpins autonomy, ensuring our systems understand and respond to the operational environment with speed, precision, and resilience.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Multidisciplinary Team Leadership – Lead teams across autonomy, integration, and testing by aligning technical efforts, resolving cross-functional challenges, and driving mission-focused execution.</li>\n<li>Develop advanced perception algorithms , Design and implement robust algorithms for object detection, classification, and multi-target tracking across diverse sensor modalities.</li>\n<li>Implement sensor fusion frameworks , Integrate data from vision systems, radars, and other mission sensors using probabilistic and deterministic fusion techniques to generate accurate situational awareness.</li>\n<li>Develop state estimation capabilities , Design and refine algorithms for localization and pose estimation using IMU, GPS, vision, and other onboard sensing inputs to enable stable and accurate navigation.</li>\n<li>Analyze and utilize sensor ICDs , Interpret interface control documents (ICDs) and technical specifications for aircraft-mounted sensors to ensure correct data handling, interpretation, and synchronization.</li>\n<li>Optimize perception performance , Tune and evaluate perception pipelines for performance, robustness, and real-time efficiency in both simulation and real-world environments.</li>\n<li>Support autonomy integration , Work closely with autonomy, systems, and integration teams to interface perception outputs with planning, behaviors, and decision-making modules.</li>\n<li>Validate in simulated and operational settings , Leverage synthetic data, simulation environments, and field testing to validate algorithm accuracy and mission readiness.</li>\n<li>Collaborate with hardware and sensor teams , Ensure seamless integration of perception algorithms with onboard compute platforms and diverse sensor payloads.</li>\n<li>Drive innovation in airborne sensing , Contribute novel ideas and state-of-the-art techniques to advance real-time perception capabilities for unmanned aircraft operating in complex, GPS-denied, or contested environments.</li>\n<li>Travel Requirement , Members of this team typically travel around 10-15% of the year (to different office locations, customer sites, and flight integration events).</li>\n</ul>\n<p>Required Qualifications:</p>\n<ul>\n<li>BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, and/or similar degree, or equivalent practical experience</li>\n<li>Typically requires a minimum of 7 years of related experience with a Bachelor’s degree; or 5 years and a Master’s degree; or 4 years with a PhD; or equivalent work experience</li>\n<li>5+ years of experience in Unmanned Systems programs in the DoD or applied R&amp;D</li>\n<li>2+ years of people leadership experience</li>\n<li>Background in implementing algorithms such as Kalman Filters, multi-target tracking, or deep learning-based detection models.</li>\n<li>Familiarity with fusing data from radar, EO/IR cameras, or other sensors using probabilistic or rule-based approaches.</li>\n<li>Familiarity with SLAM, visual-inertial odometry, or sensor-fused localization approaches in real-time applications.</li>\n<li>Ability to interpret and work with Interface Control Documents (ICDs) and hardware integration specs.</li>\n<li>Proficiency with version control, debugging, and test-driven development in cross-functional teams.</li>\n<li>Ability to obtain a SECRET clearance.</li>\n</ul>\n<p>Preferred Qualifications:</p>\n<ul>\n<li>Hands-on 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