As a Research Scientist Manager, you will lead a world-class team of research scientists and engineers, defining the research roadmap and driving execution from early prototyping to deployment. You'll thrive in a fast-moving environment, balancing deep technical leadership with people management, vision setting, and delivery.

Key responsibilities include:

• Leading, mentoring, and growing a team of research scientists and engineers working on GenAI research initiatives
• Defining and driving a multi-year research roadmap, identifying key scientific questions, setting milestones, allocating resources, and ensuring rigorous execution
• Collaborating cross-functionally with engineering, product, client-facing teams, and external academic or industry partners to translate research into components, insights, and actionable outcomes
• Communicating compellingly, publishing research, presenting at conferences, engaging in open-source contributions, and representing the team externally
• Driving an inclusive, high-performing culture, helping your team through technical challenges, providing growth opportunities, and attracting top talent

Ideal candidates will have:

• 5+ years of hands-on research experience in machine learning, deep learning, generative models, agent/RL systems, or related domains
• A strong track record of research excellence, including publications in top-tier ML/AI venues
• Experience leading or managing research teams, mentoring, coaching, and developing talent
• Excellent written and verbal communication skills, articulating research ideas and outcomes to technical and non-technical stakeholders

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As Director of Enterprise ML, you will lead a world-class team of research scientists and engineers, define the research roadmap, and drive execution from early prototyping to deployment. You’ll thrive in a fast-moving environment, balancing deep technical leadership with people management, vision setting, and delivery.

This role is ideal for a leader who thrives in ambiguity, understands both frontier GenAI capabilities and their limitations, and is motivated by turning research into durable, production-ready systems.

Key responsibilities include:

• Leading, mentoring, and growing a team of research scientists and engineers working on GenAI research initiatives
• Defining and driving a multi-year research roadmap, identifying key scientific questions, setting milestones, allocating resources, and ensuring rigorous execution
• Collaborating cross-functionally with engineering, product, client-facing teams, and external academic or industry partners to translate research into components, insights, and actionable outcomes
• Communicating compellingly, publishing research, presenting at conferences, engaging in open-source contributions, and representing the team externally
• Driving an inclusive, high-performing culture, helping your team through technical challenges, providing growth opportunities, and attracting top talent

Core qualifications include:

• 8+ years of hands-on research experience in machine learning, deep learning, generative models, agent/RL systems, or related domains
• A strong track record of research excellence, including publications in top-tier ML/AI venues
• Experience leading or managing research teams
• Excellent written and verbal communication skills

Nice-to-have qualifications include:

• Hands-on experience building and deploying agent-based, tool-augmented, or workflow-driven LLM systems in enterprise environments
• Prior ownership of enterprise AI platforms, internal ML products, or customer-facing AI services at scale
• Proven track record of partnering directly with enterprises to identify high-impact use cases and deliver measurable business outcomes

Compensation packages at Scale include base salary, equity, and benefits, with a salary range of $289,800-$362,250 USD for this full-time position in San Francisco, New York, and Seattle.

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Your role will span marketing, co-selling, technical support, and model launch program management to ensure a successful partnership and a direct impact on revenue growth and profitability.

As the main point of contact, you will collaborate closely with our partner companies to ensure they meet their goals, are satisfied with fal's platform, and stay informed on the latest industry trends.

Success in this role requires a combination of strong business acumen, technical understanding, and the ability to lead complex, cross-functional initiatives.

Key responsibilities include:

• Contract Negotiations & Procurement: Lead complex commercial negotiations, including Master Service Agreements (MSAs), NRE, cost models, open-book pricing, ramp and capacity commitments, and risk-buy frameworks.

• Partner Relationship Management: Develop and manage strong relationships with key stakeholders at partner companies, ensuring alignment on business goals and fostering long-term, mutually beneficial success.

• Drive Model Launches: Oversee the successful deployment and scaling of generative AI models with partners, including coordination with technical teams to ensure seamless execution.

• Infrastructure Development: Work with partners to design and implement infrastructure solutions that support their AI model deployments, ensuring reliability, scalability, and performance.

• Co-Selling and Marketing: Collaborate with partners on joint marketing initiatives, co-sell opportunities, and product integrations to drive revenue growth.

• Program Management: Lead the execution of the Model Launch Program, managing timelines, deliverables, and cross-functional teams to ensure that models are launched successfully and on schedule.

• Drive Revenue and KPIs: Focus on driving revenue growth and profitability with your partner portfolio. You will be responsible for meeting specific KPIs, including consumption-based revenue targets, partner satisfaction, and expansion opportunities.

• Industry Insight: Stay informed about trends in AI, machine learning, and generative media to proactively identify opportunities and provide thought leadership to partners.

• Partner Success: Ensure partners have the resources and support they need to succeed. Manage the end-to-end partner experience to foster long-term, mutually beneficial relationships.

Requirements include:

• 4+ years of experience at an infrastructure or cloud company, with demonstrated expertise in contract negotiations and managing strategic partnerships and driving go-to-market initiatives,

• Previous experience in Account Executive (AE) roles particularly within the AI, technology, or enterprise software industries is preferred,

• Proven track record of driving partner success and managing complex, multi-stakeholder initiatives from inception to execution.

• Strong understanding of AI infrastructure, generative models, and model deployment processes.

• Experience in co-selling, joint marketing, and building go-to-market strategies with external partners.

• Expertise in program management with the ability to manage multiple complex projects simultaneously, meeting deadlines and delivering results.

• Strong communication skills, with the ability to engage technical and non-technical audiences and lead cross-functional teams.

• A proactive, problem-solving mindset with the ability to adapt to a fast-changing landscape.

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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.

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.

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.

Responsibilities

• Embody our vision of an interdisciplinary environment and embrace learning about areas outside of your traditional area of expertise

• Develop, implement, train, and iteratively improve state-of-the-art models for biomolecule design

• Analyze, visualize, and communicate results to support team efforts in improving models and data

• Create, deploy, and refine tools for efficient, reliable machine learning experimentation and production

• Work with biologists to collect data for the training and evaluation of generative models of biomolecules

• Provide mentorship and technical direction to team members as appropriate

Qualifications

• 3+ years of hands-on experience developing ML models

• Demonstrated track record of implementing, training, improving advanced machine learning models

• Highly capable programmer fluent in Python ecosystem and PyTorch or similar deep learning framework

• Availability to work with team members across US and Europe, with meetings starting at 8am PT and ending at 7pm CET

• Readiness to travel several times a year for company retreats and business events

Compensation

$200K – $275K + Bonus + Equity

Benefits

• A competitive compensation package

• 30 days paid vacation per year

• Comprehensive health insurance for US based employees

• 401K with company match for US based employees and Direktversicherung for German employees

• Quarterly company-wide retreats

• Monthly wellness benefit

• Budget for multiple visits per year to our offices in Berlin, Palo Alto or Switzerland

• Learning & Development budget to attend conferences, take courses, or otherwise invest in your professional growth, as well as access to the Learning & Development platform EdX and Hone

• A buddy to help you get settled

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.

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We're a multidisciplinary team building state-of-the-art generative models in chemistry & materials to accelerate scientific breakthroughs.

As a Research Scientist in our Science unit, you will be at the forefront of applying generative AI to the "Grand Challenge" of predicting the structure and properties of complex matter.

Your work will bridge the gap between in silico modeling and real-world laboratory discovery, particularly in areas where traditional computational methods are bottlenecked by time and complexity.

Key responsibilities:

• Design and train advanced AI models (transformers, generative models, etc.) to model the structure and properties of complex physical systems.

• Develop deep understanding of scientific domains that can be used to identify novel modelling approaches.

• Design and execute robust ML experiments that allow for the accumulation of small improvements, sharing results through clear verbal and written communication.

• Collaborate with other scientists and engineers to help shape the research roadmap.

About You

You are fascinated by the intersection of AI and natural science and determined to help solve grand challenges facing humanity.

In order to set you up for success as a Research Scientist at Google DeepMind, we look for the following skills and experience:

• PhD / equivalent experience in computer science, computational chemistry, materials science, physics with a focus on atomistic simulation or structural biology.

• Fluency in generative models and transformers

• Excellent collaboration and communication skills

• Experience with modern deep learning frameworks

In addition, the following would be an advantage:

• Record of high-impact published work at the intersection of AI and natural science, particularly chemistry and materials science.

• Demonstrated experience in geometric deep learning.

Applications close on Monday 20th April at 5pm BST

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We're looking for a Research Scientist to join our Science unit, where you'll be at the forefront of applying generative AI to predict the structure and properties of complex matter.

As a Research Scientist, you'll design and train advanced AI models to model the structure and properties of complex physical systems, develop a deep understanding of scientific domains, and collaborate with other scientists and engineers to shape the research roadmap.

Key responsibilities include designing and executing robust ML experiments, accumulating small improvements, and sharing results through clear verbal and written communication.

To succeed in this role, you'll need a PhD or equivalent experience in computer science, computational chemistry, materials science, physics, or a related field, with fluency in generative models and transformers.

Experience with modern deep learning frameworks and a record of high-impact published work at the intersection of AI and natural science are also advantageous.

Applications close on Monday 20th April at 5pm BST.

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Our ideal candidate has strong systems skills in configuring and troubleshooting complex distributed data processing systems for maximum performance. They should be able to build bespoke data processing systems from scratch, prepare pre-training and post-training data for state-of-the-art large language models and generative models, and organise and meticulously bookkeep data across multiple clouds, modalities, and sources.

In this role, you will work closely with our team to contribute directly to our mission and deliver excellence. You will be expected to have strong communication skills, be able to concisely and accurately share knowledge with your teammates, and demonstrate initiative and leadership.

The base salary for this position is $180,000 - $440,000 USD, and we offer a comprehensive total rewards package including equity, medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks.

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While experience in one or more of the following areas is a strong plus: large-scale model training, research in 3D computer vision.

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.

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.

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.

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.

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Member of Technical Staff - Image / Video Generation

Job Description

We're the team behind Latent Diffusion, Stable Diffusion, and FLUX,foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video,tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we’re just getting started.

Why This Role

You'll train large-scale diffusion models for image and video generation, exploring new approaches while maintaining the rigor that helps us distinguish meaningful progress from incremental tweaks. This isn't about following established recipes,it's about running the experiments that clarify which architectural choices matter and which are less impactful.

What You’ll Work On

• Trains large-scale diffusion transformer models for image and video data, working at the scale where intuitions break and empirical evidence matters
• Rigorously ablates design choices,running experiments that isolate variables, control for confounds, and produce insights you can actually trust,then communicating those results to shape our research direction
• Reasons about the speed-quality tradeoffs of neural network architectures in production settings where both constraints matter simultaneously
• Fine-tunes diffusion models for specialized applications like image and video upscalers, inpainting/outpainting models, and other tasks where general-purpose models aren't enough

What We’re Looking For

• You've trained large-scale diffusion models and developed strong intuitions about what matters. You know that at research scale, every design choice has tradeoffs, and the only way to know which ones are worth making is through careful ablation. You're comfortable debugging distributed training issues and presenting research findings to the team.

Required Skills

• Hands-on experience training large-scale diffusion models for image and video data, with practical knowledge of common failure modes and what matters most in training
• Experience fine-tuning diffusion models for specialized applications,upscalers, inpainting, outpainting, or other tasks where understanding the domain matters as much as understanding the architecture
• Deep understanding of how to effectively evaluate image and video generative models,knowing which metrics correlate with quality and which are just convenient proxies
• Strong proficiency in PyTorch, transformer architectures, and the full ecosystem of modern deep learning
• Solid understanding of distributed training techniques,FSDP, low precision training, model parallelism,because our models don't fit on one GPU and training decisions impact research outcomes

Preferred Skills

• Experience writing forward and backward Triton kernels and ensuring their correctness while considering floating point errors
• Proficiency with profiling, debugging, and optimizing single and multi-GPU operations using tools like Nsight or stack trace viewers
• Know the performance characteristics of different architectural choices at scale
• Have published research that contributed to how people think about generative models

How We Work Together

We’re a distributed team with real offices that people actually use. Depending on your role, you’ll either join us in Freiburg or SF at least 2 days a week (or one full week every other week), or work remotely with a monthly in-person week to stay connected. We’ll cover reasonable travel costs to make this possible. We think in-person time matters, and we’ve structured things to make it accessible to all. We’ll discuss what this will look like for the role during our interview process.

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At Google DeepMind, we've built a unique culture and work environment where long-term ambitious research can flourish. Our special interdisciplinary team combines the best techniques from deep learning, reinforcement learning, and systems neuroscience to build general-purpose learning algorithms.

Research Scientists lead our efforts in developing novel tools, infrastructure, and algorithms towards the end goal of solving and building Artificial General Intelligence.

unparalleled opportunities to work with a talented team of researchers and engineers.

Drawing on expertise from a variety of disciplines including deep learning, computer vision, language modeling, and advanced generative architectures, our Research Scientists are at the forefront of groundbreaking research.

Responsibilities:

• Design, rapidly implement, and rigorously evaluate cutting-edge deep learning algorithms and data curation for multimodal generative AI, with a particular emphasis on culturally-adapted image and video synthesis.
• Report and present research findings and developments clearly and efficiently both internally and externally, verbally and in writing.
• Suggest and engage in team collaborations to meet ambitious research goals, while also driving significant individual contributions.
• Work in collaboration with our Ethics and Governance teams to ensure our advances in intelligence are developed ethically and provide broad benefits to humanity.

Requirements:

• PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, or equivalent practical experience.
• 2+ years of relevant experience in deep learning research and development, particularly in generative AI and related to image and video synthesis. This includes diffusion models and autoregressive generative models.
• Experience in software development with one or more programming languages (e.g., Python) and deep learning frameworks (e.g., Jax, TensorFlow, PyTorch), with a track record of building high-quality research prototypes and systems.

Preferred Qualifications:

• Demonstrated experience in large-scale training of multimodal generative models.
• A track record of research or engineering achievements, including publications in peer-reviewed conferences or journals.

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At Google DeepMind, we've built a unique culture and work environment where long-term ambitious research can flourish. Our special interdisciplinary team combines the best techniques from deep learning, reinforcement learning, and systems neuroscience to build general-purpose learning algorithms.

Research Scientists lead our efforts in developing novel tools, infrastructure, and algorithms towards the end goal of solving and building Artificial General Intelligence.

Having pioneered research in the world's leading academic and industrial labs, PhDs, post-docs, or professorships, Research Scientists join Google DeepMind to work collaboratively within and across Research fields. They are expected to work with teams on large scale AI, and develop solutions to fundamental questions in machine learning and AI.

Drawing on expertise from a variety of disciplines including deep learning, computer vision, language modeling, and advanced generative architectures, our Research Scientists are at the forefront of groundbreaking research.

Responsibilities

• Design, rapidly implement, and rigorously evaluate cutting-edge deep learning algorithms and data curation for multimodal generative AI, with a particular emphasis on culturally-adapted image and video synthesis.
• Report and present research findings and developments clearly and efficiently both internally and externally, verbally and in writing.
• Suggest and engage in team collaborations to meet ambitious research goals, while also driving significant individual contributions.
• Work in collaboration with our Ethics and Governance teams to ensure our advances in intelligence are developed ethically and provide broad benefits to humanity.

Minimum Qualifications

• PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, or equivalent practical experience.
• 2+ years of relevant experience in deep learning research and development, particularly in generative AI and related to image and video synthesis. This includes diffusion models and autoregressive generative models.
• Experience in software development with one or more programming languages (e.g., Python) and deep learning frameworks (e.g., Jax, TensorFlow, PyTorch), with a track record of building high-quality research prototypes and systems.

Preferred Qualifications

• Demonstrated experience in large-scale training of multimodal generative models.
• A track record of research or engineering achievements, including publications in peer-reviewed conferences or journals.

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Key responsibilities include pioneering novel computer vision models to create a unified understanding of agriculture from diverse satellite data sources, solving core AI problems by developing generalizable models that are robust across varied agricultural systems, leading research toward the grand challenge of field-level crop yield forecasting, designing and executing large-scale experiments, writing high-quality, reusable code, and contributing to a production-ready system.

You will also mentor junior researchers, collaborate with cross-functional teams across Google, and publish your work at top-tier conferences.

We are looking for a passionate and talented researcher with a strong foundation and a proven ability to conduct impactful research in AI. You should have a PhD or equivalent practical research experience in Computer Science, AI, or a related field with a focus on computer vision and/or machine learning, a strong publication record in top-tier AI conferences, hands-on experience building and training deep learning models in frameworks such as JAX, TensorFlow, or PyTorch, and demonstrated expertise in one or more of the following: generative models, segmentation algorithms, multi-modal fusion, spatio-temporal analysis.

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The role of the Research Engineer will be to develop state-of-the-art methods for multimodal generative AI models, with a primary focus on image generation and editing. This role is for the team behind “Nano Banana”.

As a Research Engineer at Google DeepMind, you will lead our efforts in developing novel tools, infrastructure, and algorithms towards the end goal of solving and building Artificial General Intelligence. You will work collaboratively within and across Research fields, drawing on expertise from a variety of disciplines including deep learning, computer vision, language modeling, and advanced generative architectures.

Key responsibilities include designing, implementing, and evaluating cutting-edge deep learning algorithms, data curation, and evaluation infrastructure for multimodal generative AI, with a particular emphasis on image synthesis. You will report and present research findings and developments clearly and efficiently both internally and externally, verbally and in writing. You will also suggest and engage in team collaborations to meet ambitious research goals, while also driving significant individual contributions.

To succeed as a Research Engineer at Google DeepMind, we look for the following skills and experience:

• PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, or equivalent practical experience.
• Proven experience in deep learning research and development, particularly in generative AI and related to image synthesis. This includes diffusion models and autoregressive generative models. Experience with post-training is a plus.
• Exceptional engineering skills in Python and deep learning frameworks (e.g., Jax, TensorFlow, PyTorch), with a track record of building high-quality research prototypes and systems.
• Strong publication record at top-tier machine learning, computer vision, and graphics conferences (e.g., NeurIPS, ICLR, ICML, SIGGRAPH, CVPR, ICCV).

In addition, the following would be an advantage:

• Demonstrated experience in multimodal generative modeling, especially combining large language models with visual generation (e.g., text-to-image/video systems, joint autoregressive and diffusion models).
• A keen eye for visual aesthetics and detail, coupled with a passion for creating high-quality, visually compelling generative content.
• A real passion for AI!

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As an Open-Source ML engineer in Computer Vision, you will work mainly with existing open-source libraries, such as Transformers and Datasets to boost the support for vision or multi-modal models and datasets. You will bring your computer vision expertise to provide the best computer-vision tool stack in the machine learning ecosystem and work with us to provide the best, simplest, and most intuitive computer-vision library in the industry.

Responsibilities:

• Work with existing open-source libraries to boost support for vision or multi-modal models and datasets.
• Bring computer vision expertise to provide the best computer-vision tool stack in the machine learning ecosystem.
• Collaborate with researchers, ML practitioners, and data scientists on a daily basis.
• Foster one of the most active machine learning communities, helping users contribute to and use the tools that you build.

Requirements:

• Deep expertise in computer vision: object detection, segmentation, generative models, or multimodal systems.
• Strong open-source presence: You’ve contributed significantly to CV libraries (e.g., OpenCV, Detectron2, MMDetection, or Hugging Face’s own transformers/diffusers), as a Core-Contributor or maintainer.
• Scalability mindset: Experience optimizing models for production, deploying at scale, or improving inference efficiency.
• Collaboration & mentorship: You enjoy working with cross-functional teams, reviewing PRs, and guiding junior contributors.
• Alignment with our mission: You believe in democratizing AI and want to empower millions of builders with state-of-the-art tools.

If you love open-source, are passionate about the new development of Transformers models in computer vision, have experience building, optimizing, and training such models in PyTorch and/or TensorFlow, serving them in production, and want to contribute to one of the fastest-growing ML libraries, then we can't wait to see your application!

If you're interested in joining us, but don't tick every box above, we still encourage you to apply! We're building a diverse team whose skills, experiences, and backgrounds complement one another. We're happy to consider where you might be able to make the biggest impact.

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We are building the fastest growing platform for AI builders with over 11 million users who collectively shared over 2M models, 700k datasets & 600k apps.

As an Open-Source ML engineer in Computer Vision, you will work mainly with existing open-source libraries, such as Transformers and Datasets to boost the support for vision or multi-modal models and datasets.

You will bring your computer vision expertise to provide the best computer-vision tool stack in the machine learning ecosystem and work with us to provide the best, simplest, and most intuitive computer-vision library in the industry.

You'll get to foster one of the most active machine learning communities, helping users contribute to and use the tools that you build.

You'll interact with Researchers, ML practitioners, and data scientists on a daily basis through GitHub, our forums, or slack.

Responsibilities

• Deep expertise in computer vision: object detection, segmentation, generative models, or multimodal systems.
• Strong open-source presence: You’ve contributed significantly to CV libraries (e.g., OpenCV, Detectron2, MMDetection, or Hugging Face’s own transformers/diffusers), as a Core-Contributor or maintainer.
• Scalability mindset: Experience optimizing models for production, deploying at scale, or improving inference efficiency.
• Collaboration & mentorship: You enjoy working with cross-functional teams, reviewing PRs, and guiding junior contributors.
• Alignment with our mission: You believe in democratizing AI and want to empower millions of builders with state-of-the-art tools.

Benefits

• Flexible working hours and remote options.
• Health, dental, and vision benefits for employees and their dependents.
• Parental leave and flexible paid time off.
• Reimbursement for relevant conferences, training, and education.
• Company equity as part of their compensation package.

What We Offer

• Work with some of the smartest people in our industry.
• A bias for impact and a continuous growth mindset.
• Support for your well-being and career development.
• Opportunities to visit our offices in NYC and Paris.
• An outfitting of your workstation to ensure success.

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Research Engineer/Research Scientist, RL/Reasoning

Location

San Francisco

Employment Type

Full time

Department

Research

Compensation

• $295K – $445K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

The RL and Reasoning team drives the core reasoning paradigm and has created groundbreaking innovations such as o1 and o3. They focus on pushing the boundaries of reinforcement learning research, building next-generation generative models, and deploying them at scale.

About the Role

As a Research Engineer/Research Scientist at OpenAI, you will advance the frontier of AI alignment and capabilities through cutting-edge RL methods. Your work will sit at the heart of training intelligent, aligned, and general-purpose agents, including the systems that power various models.

We’re looking for people who have a background in reinforcement learning research, are able to iterate quickly, and are proficient at coding.

This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

You might thrive in this role if:

• You love being on the cutting edge of RL and language model research.

• You’re a self-starter who takes initiative and ownership of ideas, driving them to completion.

• You value principled approaches, simple experiments in tightly-controlled settings, and reaching trustworthy conclusions which stand the test of time.

• You thrive in a fast-paced, dynamic, and technically complex environment where rapid iteration is key.

• You’re comfortable diving into a large ML codebase to debug and improve it.

• You have a deep understanding of machine learning and machine learning applications.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

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San Mateo, CA, United States

Early Career

ID: 5471

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.

At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device.

We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there.

A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone.

Recommendation Systems are a key growth lever at Roblox, driving retention, engagement, and monetization for hundreds of millions of users. This role offers the unique opportunity to redefine how users search and discover everything from the most interesting immersive experiences and digital avatars in our Marketplace to personalized advertising. You will solve a diverse range of high-scale ranking, retrieval, and personalization problems across our platform.

We combine cutting-edge research —including deep learning, generative AI, and reinforcement learning techniques— with large-scale engineering to bridge experimentation and production; you'll design algorithms that operate at massive scale and shape the next generation of recommender systems for user-generated content.

Teams Hiring for This Role

• Search: powers major recommendation surfaces—drives user engagement by redesigning core surfaces and search/homepage ranking

• Notifications: owns the distributed systems and ML platform that transform billions of Roblox signals into high‑value notifications for hundreds of millions of players.

• Economy: builds the ML backbone for marketplace, monetization, and commerce (including fraud, pricing, and bundling)

• Ads & Brands: focuses on ranking, retrieval, and marketplace/auction theory to optimize sponsored content delivery.

• Safety, Alt Defense: architects a massive-scale detection engine that identifies recidivist bad actors across billions of accounts to ensure the long-term integrity of the Roblox community.

You Will

• Design and implement large-scale recommendation systems that power discovery across Roblox’s surfaces — experiences, avatars, and creator content.

• Develop deep learning models for ranking, retrieval, and personalization using approaches in multimodal models, LLMs, and generative AI.

• Collaborate with applied researchers, engineers, and product teams to advance experimentation and accelerate innovation.

• Translate research into production systems that impact hundreds of millions of daily active users.

• Work backward from user and product needs to deliver ML solutions that drive engagement, retention, and ecosystem growth.

You Have

• Possessing or pursuing a PhD in computer science, engineering, or a related field, with a thesis aligned to Roblox’s research areas.

• Expertise in one or more areas: recommender systems, search systems, information retrieval, or generative models (e.g., LLMs, VLMs, VLAs)

• Ability to design and architect systems for efficient personalization and user interest modeling using advanced attention mechanisms (e.g., sparse/linear attention).

• A strong research track record, evidenced by multiple publications and presentations in top-tier, peer-reviewed venues (e.g., SIGIR, KDD, RecSys, ICLR, ICML, NeurIPS)

• Proficiency in one or more programming languages (e.g., Python, C++, Go, Java) and experience building and optimizing large-scale systems.

You may redact age, date of birth, and dates of attendance/graduation from your resume if you prefer.

As you apply, you can find more information about our process by signing up for Speak\_. You'll gain access to our practice assessment, comprehensive guides, FAQs, and modules designed to help you ace the hiring process.

For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on this page.

Annual Salary Range

$195,780—$242,100 USD

Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).

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