{"version":"0.1","company":{"name":"YubHub","url":"https://yubhub.co","jobsUrl":"https://yubhub.co/jobs/skill/feature-engineering"},"x-facet":{"type":"skill","slug":"feature-engineering","display":"Feature Engineering","count":8},"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_467be5c4-940"},"title":"Machine Learning Engineer","description":"<p>We&#39;re looking for a Machine Learning Engineer to join our Ads Engineering team. As a Machine Learning Engineer at Reddit, 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 works on high-impact systems that operate at internet scale and directly influence user experience, advertiser value, and business outcomes. You&#39;ll work on complex, real-world ML problems at massive scale, and contribute to technical strategy, architecture, and long-term ML roadmap.</p>\n<p>Responsibilities:</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.</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. 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.</p>\n<p>The base salary range for this position is: $185,800-$260,100 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_467be5c4-940","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/7131932","x-work-arrangement":"remote","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":"$185,800-$260,100 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"],"datePosted":"2026-04-18T15:57:49.850Z","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","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":185800,"maxValue":260100,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_27ecbf0a-523"},"title":"Senior GTM Data Scientist","description":"<p>We are building a GTM Data Products team to embed machine learning and AI directly into our Sales and Marketing workflows. We are hiring a Senior GTM Data Scientist to design and deploy predictive systems that materially improve customer acquisition, sales efficiency, and customer retention and expansion.</p>\n<p>This is not a reporting role. This role owns end-to-end data products - from problem framing and modeling to deployment and operational integration - that directly influence how our GTM organization prioritizes leads, manages accounts, allocates resources, and drives revenue.</p>\n<p>You’ll work closely with Marketing, Sales, and RevOps leadership to build ML-powered systems that change how decisions are made at scale.</p>\n<p><strong>What will I be doing?</strong></p>\n<ul>\n<li>Build Revenue-Impacting ML Systems</li>\n<li>Develop, deploy, optimize predictive models (lead scoring, account prioritization, marketing attribution, revenue estimation)</li>\n<li>Productionize models into operational systems (Salesforce, Marketo, outbound workflows)</li>\n<li>Monitor model performance and iterate for measurable business lift</li>\n<li>Design and implement experimentation frameworks (A/B testing, holdouts, incremental lift measurement)</li>\n<li>Apply advanced techniques when appropriate (e.g., causal inference, uplift modeling, segmentation, LTV modeling)</li>\n</ul>\n<p>You don’t just build models - you ensure they change behavior.</p>\n<p><strong>Own End-to-End Data Products</strong></p>\n<ul>\n<li>Translate ambiguous business problems into clear, measurable objectives</li>\n<li>Define GTM data products vision, success metrics, and roadmap</li>\n<li>Ensure integration into existing workflows and systems</li>\n<li>Lead stakeholder alignment and change management</li>\n<li>Secure buy-in from system owners before replacing or enhancing existing solutions</li>\n</ul>\n<p>You operate as a mini GM for your data products.</p>\n<p><strong>Architect Scalable Data Foundations</strong></p>\n<ul>\n<li>Design robust data pipelines and modeling infrastructure in collaboration with Data Engineering / Data Infrastructure</li>\n<li>Ensure data quality, governance, and reproducibility</li>\n<li>Elevate the team’s standards for experimentation, documentation, and knowledge sharing</li>\n<li>Push adoption of new tools and AI capabilities where appropriate</li>\n</ul>\n<p>You raise the technical bar for the GTM organization.</p>\n<p><strong>What impact might I have?</strong></p>\n<p>Within 6-12 months, you might:</p>\n<ul>\n<li>Launch predictive models that materially improve conversion, expansion, or retention</li>\n<li>Reduce inefficiencies in Sales workflows through automation</li>\n<li>Help leadership make investment decisions backed by rigorous data science</li>\n<li>Influence GTM strategy through quantitative insight and modeling</li>\n</ul>\n<p>Success is measured in business outcomes - not dashboards built.</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_27ecbf0a-523","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Intercom","sameAs":"https://www.intercom.com/","logo":"https://logos.yubhub.co/intercom.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/intercom/jobs/7652268","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$197,600 - $246,713","x-skills-required":["Expert-level SQL","Advanced Python or R for modeling and experimentation","Strong foundation in statistics and experimental design","Predictive modeling","Feature engineering"],"x-skills-preferred":["Causal inference or uplift modeling","Model deployment & monitoring"],"datePosted":"2026-04-18T15:55:52.628Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, California"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Expert-level SQL, Advanced Python or R for modeling and experimentation, Strong foundation in statistics and experimental design, Predictive modeling, Feature engineering, Causal inference or uplift modeling, Model deployment & monitoring","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":197600,"maxValue":246713,"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_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. Demonstrated experience architecting ads marketplace design, improving and optimizing ads auction and pricing mechanisms.</li>\n<li>Solid understanding of large-scale data processing, distributed computing, and data infrastructure (e.g., Spark, Kafka, Beam, Flink).</li>\n<li>Proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries for feature engineering, model training, and inference.</li>\n<li>Proficiency with programming languages (Java, Python, Golang, C++, or similar) and statistical analysis.</li>\n<li>Proven technical leadership in cross-functional settings, driving architectural decisions and influencing stakeholders (product, data science, privacy, legal).</li>\n<li>Excellent communication, mentoring, and collaboration skills to align teams on a long-term vision for ads marketplace optimization.</li>\n</ul>\n<p>Benefits:</p>\n<ul>\n<li>Comprehensive Healthcare Benefits</li>\n<li>401k Matching</li>\n<li>Workspace benefits for your home office</li>\n<li>Personal &amp; 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_586b9fef-509"},"title":"Senior Software Engineer - Network Enablement (Applied ML)","description":"<p>We believe that the way people interact with their finances will drastically improve in the next few years. We&#39;re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products.</p>\n<p>On this team, you will build and operate the ML infrastructure and product services that enable trust and intelligence across Plaid&#39;s network. You&#39;ll own feature engineering, offline training and batch scoring, online feature serving, and real-time inference so model outputs directly power partner-facing fraud &amp; trust products and bank intelligence features.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Embed model inference into Network Enablement product flows and decision logic (APIs, feature flags, backend flows).</li>\n<li>Define and instrument product + ML success metrics (fraud reduction, retention lift, false positives, downstream impact).</li>\n<li>Design and run experiments and rollout plans (backtesting, shadow scoring, A/B tests, feature-flagged releases) to validate product hypotheses.</li>\n<li>Build and operate offline training pipelines and production batch scoring for bank intelligence products.</li>\n<li>Ship and maintain online feature serving and low-latency model inference endpoints for real-time partner/bank scoring.</li>\n<li>Implement model CI/CD, model/version registry, and safe rollout/rollback strategies.</li>\n<li>Monitor model/data health: drift/regression detection, model-quality dashboards, alerts, and SLOs targeted to partner product needs.</li>\n<li>Ensure offline and online parity, data lineage, and automated validation / data contracts to reduce regressions.</li>\n<li>Optimize inference performance and cost for real-time scoring (batching, caching, runtime selection).</li>\n<li>Ensure fairness, explainability and PII-aware handling for partner-facing ML features; maintain auditability for compliance.</li>\n<li>Partner with platform and cross-functional teams to scale the ML/data foundation (graph features, sequence embeddings, unified pipelines).</li>\n<li>Mentor engineers and document team standards for ML productization and operations.</li>\n</ul>\n<p><strong>Qualifications</strong></p>\n<ul>\n<li>Must-haves:</li>\n<li>Strong software engineering skills including systems design, APIs, and building reliable backend services (Go or Python preferred).</li>\n<li>Production experience with batch and streaming data pipelines and orchestration tools such as Airflow or Spark.</li>\n<li>Experience building or operating real-time scoring and online feature-serving systems, including feature stores and low-latency model inference.</li>\n<li>Experience integrating model outputs into product flows (APIs, feature flags) and measuring impact through experiments and product metrics.</li>\n<li>Experience with model lifecycle and operations: model registries, CI/CD for models, reproducible training, offline &amp; online parity, monitoring and incident response.</li>\n<li>Nice to have:</li>\n<li>Experience in fraud, risk, or marketing intelligence domains.</li>\n<li>Experience with feature-store products (Tecton / Chronon / Feast / internal) and unified pipelines.</li>\n<li>Experience with graph frameworks, graph feature engineering, or sequence embeddings.</li>\n<li>Experience optimizing inference at scale (Triton/ONNX/quantization, batching, caching).</li>\n</ul>\n<p><strong>Additional Information</strong></p>\n<p>Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable.</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_586b9fef-509","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Plaid","sameAs":"https://plaid.com/","logo":"https://logos.yubhub.co/plaid.com.png"},"x-apply-url":"https://jobs.lever.co/plaid/43b1374d-5c5e-4b63-b710-a95e3cb76bbe","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$190,800-$286,800 per year","x-skills-required":["software engineering","systems design","APIs","backend services","Go","Python","batch and streaming data pipelines","orchestration tools","Airflow","Spark","real-time scoring","online feature-serving systems","feature stores","low-latency model inference","model outputs","product flows","experiments","product metrics","model lifecycle","operations","model registries","CI/CD","reproducible training","offline & online parity","monitoring","incident response"],"x-skills-preferred":["fraud","risk","marketing intelligence","feature-store products","unified pipelines","graph frameworks","graph feature engineering","sequence embeddings","inference at scale","Triton","ONNX","quantization","batching","caching"],"datePosted":"2026-04-17T12:51:26.228Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"software engineering, systems design, APIs, backend services, Go, Python, batch and streaming data pipelines, orchestration tools, Airflow, Spark, real-time scoring, online feature-serving systems, feature stores, low-latency model inference, model outputs, product flows, experiments, product metrics, model lifecycle, operations, model registries, CI/CD, reproducible training, offline & online parity, monitoring, incident response, fraud, risk, marketing intelligence, feature-store products, unified pipelines, graph frameworks, graph feature engineering, sequence embeddings, inference at scale, Triton, ONNX, quantization, batching, caching","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":190800,"maxValue":286800,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_62efca6f-b6f"},"title":"Senior AI Engineer","description":"<p>We&#39;re looking for a Senior AI Engineer who is obsessed with building AI systems that actually work in production: reliable, observable, cost-efficient, and genuinely useful. This is not a research role. You will ship AI-powered features that process real financial data for real businesses.</p>\n<p>LLM &amp; AI Pipeline Engineering - Design, build, and maintain production-grade LLM integration pipelines , including retrieval-augmented generation (RAG), prompt engineering, output parsing, and chain orchestration.</p>\n<p>Develop and operate AI features within Jeeves&#39;s core financial products: spend categorization, document extraction, anomaly detection, financial Q&amp;A, and automated reconciliation.</p>\n<p>Implement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases.</p>\n<p>Evaluate and integrate AI frameworks and tools (LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases) and advocate for the right tool for the job.</p>\n<p>Establish prompt versioning and evaluation practices to ensure AI outputs remain accurate and consistent as models and data evolve.</p>\n<p>Retrieval &amp; Vector Search - Design and maintain vector search pipelines using databases such as Pinecone, Weaviate, or pgvector to power semantic search and RAG-based features.</p>\n<p>Build document ingestion and chunking pipelines for Jeeves&#39;s financial data , processing invoices, receipts, policy documents, and transaction records.</p>\n<p>Optimize retrieval quality through embedding model selection, chunk strategy, metadata filtering, and re-ranking techniques.</p>\n<p>ML Model Serving &amp; Operations - Collaborate with data scientists to take trained ML models from experimental notebooks to production serving infrastructure.</p>\n<p>Build and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring.</p>\n<p>Implement model performance monitoring and data drift detection to ensure production models remain accurate over time.</p>\n<p>Support model retraining workflows by designing clean data pipelines and feature engineering that can be continuously updated.</p>\n<p>Backend Integration &amp; Reliability - Integrate AI services cleanly with Jeeves&#39;s backend microservices , designing clear API contracts, circuit breakers, and graceful degradation patterns.</p>\n<p>Write high-quality, testable backend code in Python or Go/Node.js to power AI-integrated features.</p>\n<p>Instrument AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility.</p>\n<p>Collaboration &amp; Growth - Partner with Product, Backend Engineering, and Data Science to define the AI roadmap and translate requirements into reliable systems.</p>\n<p>Contribute to a culture of quality by writing design docs, reviewing peers&#39; AI system designs, and sharing learnings openly.</p>\n<p>Help grow the AI engineering practice at Jeeves by establishing patterns, tooling, and best practices that the broader team can build on.</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_62efca6f-b6f","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Jeeves","sameAs":"https://www.jeeves.com/","logo":"https://logos.yubhub.co/jeeves.com.png"},"x-apply-url":"https://jobs.lever.co/tryjeeves/ded9e04e-f18e-4d4c-ae43-4b7882c6200b","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["LLM","AI","Python","LangChain","LlamaIndex","OpenAI API","Anthropic API","HuggingFace","vector databases","Pinecone","Weaviate","pgvector","semantic search","RAG-based features","document ingestion","chunking pipelines","embedding model selection","chunk strategy","metadata filtering","re-ranking techniques","model serving infrastructure","latency SLOs","input validation","output monitoring","model performance monitoring","data drift detection","clean data pipelines","feature engineering","API contracts","circuit breakers","graceful degradation patterns","structured logging","distributed tracing","latency dashboards","alerting"],"x-skills-preferred":[],"datePosted":"2026-04-17T12:39:23.341Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"India"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Finance","skills":"LLM, AI, Python, LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases, Pinecone, Weaviate, pgvector, semantic search, RAG-based features, document ingestion, chunking pipelines, embedding model selection, chunk strategy, metadata filtering, re-ranking techniques, model serving infrastructure, latency SLOs, input validation, output monitoring, model performance monitoring, data drift detection, clean data pipelines, feature engineering, API contracts, circuit breakers, graceful degradation patterns, structured logging, distributed tracing, latency dashboards, alerting"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_019ba3f3-88c"},"title":"Staff Engineer – AI/ML & Digital Twin","description":"<p><strong>Job Description</strong></p>\n<p>We are seeking a highly motivated Staff Engineer to join our team, focusing on AI/ML and Digital Twin technologies. As a Staff Engineer, you will lead and execute technical engagements across the customer lifecycle, including discovery, solution development, demonstrations, evaluations, and deployment.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Lead and execute technical engagements across the customer lifecycle, including discovery, solution development, demonstrations, evaluations, and deployment.</li>\n<li>Engage directly with customers to understand engineering workflows, data availability, and decision-making processes, translating them into AI-enabled simulation and digital engineering solutions.</li>\n<li>Develop and implement differentiated solutions using technologies such as automation, reduced order modeling, optimization, simulation democratization, system-level modeling, and digital twins.</li>\n<li>Integrate machine learning models within simulation and digital twin pipelines to improve prediction accuracy, reduce computational cost, and enable near real-time insights.</li>\n<li>Define and deliver automated and scalable workflows that reduce reliance on expert-driven simulation and enable broader adoption across engineering teams.</li>\n<li>Lead or contribute to first-of-a-kind or ambiguous use cases, including AI-assisted design exploration, surrogate modeling, and digital twin deployment.</li>\n<li>Collaborate closely with product development teams to influence roadmap, validate new capabilities, and improve usability of AI-enabled features.</li>\n<li>Deliver professional services, training, and technical guidance to ensure successful adoption of advanced workflows.</li>\n<li>Support pre-sales and technical marketing activities through demonstrations, evaluations, and industry engagement.</li>\n</ul>\n<p><strong>Benefits</strong></p>\n<ul>\n<li>Enable customers to transition from traditional simulation to AI-augmented and automated engineering workflows.</li>\n<li>Reduce time-to-insight through surrogate modeling, optimization, and intelligent automation.</li>\n<li>Expand access to simulation by supporting democratization across engineering and non-expert users.</li>\n<li>Drive adoption of digital twin technologies for predictive and operational decision-making.</li>\n<li>Influence product direction by connecting real-world use cases with next-generation AI-enabled capabilities.</li>\n<li>Contribute to business growth through high-impact technical engagements and solution delivery.</li>\n</ul>\n<p><strong>Requirements</strong></p>\n<ul>\n<li>MS (or PhD) in Engineering, Computer Science, Applied Mathematics, or related field.</li>\n<li>5+ years of experience in engineering systems, simulation, or data-driven modeling.</li>\n<li>Strong programming skills (Python preferred).</li>\n<li>Experience working with modeling, simulation, optimization, or data-driven engineering workflows.</li>\n<li>Strong analytical, problem-solving, and communication skills.</li>\n<li>Ability to operate effectively in a customer-facing, consultative engineering role.</li>\n<li>Proven experience in automation of engineering workflows or pipelines using tools such as optiSLang, modeFrontier, HEEDS or equivalent.</li>\n<li>Demonstrated expertise applying machine learning techniques in engineering contexts, including surrogate modeling, regression methods, or neural networks (CNNs, RNNs, autoencoders).</li>\n<li>Understanding of projection-based ROMs, dimensionality reduction, and feature engineering.</li>\n<li>Knowledge of multi-fidelity system modeling using Twin Builder, Simulink, AMESim or equivalent.</li>\n<li>Familiarity with deployment and operationalization of AI models, including integration into engineering workflows and use of frameworks such as PyTorch, TensorFlow, scikit-learn, Kubernetes, AWS/Azure equivalent.</li>\n<li>Exposure to cloud or HPC-based environments for large-scale simulation or data processing.</li>\n</ul>\n<p><strong>Who We Are Looking For</strong></p>\n<ul>\n<li>Customer-focused and able to build trusted relationships.</li>\n<li>Comfortable working in ambiguous, fast-evolving technical environments.</li>\n<li>A strong communicator who can translate complex concepts into actionable insights.</li>\n<li>Self-driven, organized, and capable of managing multiple priorities.</li>\n<li>A collaborative team player who contributes to a culture of learning and innovation.</li>\n</ul>\n<p><strong>The Team You’ll Be A Part Of</strong></p>\n<p>You will be part of a multidisciplinary engineering team focused on advancing industry adoption of simulation through AI, automation, digital twin, and MBSE technologies. The team collaborates closely with customers, product development, and go-to-market functions to deliver innovative, high-impact solutions.</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_019ba3f3-88c","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Ansys, Part of Synopsys","sameAs":"https://careers.synopsys.com","logo":"https://logos.yubhub.co/careers.synopsys.com.png"},"x-apply-url":"https://careers.synopsys.com/job/canonsburg/staff-engineer-ai-ml-and-digital-twin/44408/93512568768","x-work-arrangement":"Remote Eligible","x-experience-level":"Staff","x-job-type":"Employee","x-salary-range":"$112000-$168000","x-skills-required":["Python","Automation","Reduced Order Modeling","Optimization","Simulation Democratization","System-Level Modeling","Digital Twins","Machine Learning","Surrogate Modeling","Regression Methods","Neural Networks","Projection-Based ROMs","Dimensionality Reduction","Feature Engineering","Multi-Fidelity System Modeling","Cloud or HPC-Based Environments"],"x-skills-preferred":[],"datePosted":"2026-04-05T13:16:27.451Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"United States"}},"jobLocationType":"TELECOMMUTE","occupationalCategory":"Engineering","industry":"Technology","skills":"Python, Automation, Reduced Order Modeling, Optimization, Simulation Democratization, System-Level Modeling, Digital Twins, Machine Learning, Surrogate Modeling, Regression Methods, Neural Networks, Projection-Based ROMs, Dimensionality Reduction, Feature Engineering, Multi-Fidelity System Modeling, Cloud or HPC-Based Environments","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":112000,"maxValue":168000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_8b0e9386-fa9"},"title":"Data Engineering & Data Science Consultant","description":"<p><strong>Data Engineering &amp; Data Science Consultant</strong></p>\n<p>You will work hands-on on the design, build, and operationalisation of modern data and analytics solutions. You will contribute across the full lifecycle – from data ingestion and transformation to analytics, machine learning, and production deployment. You will collaborate closely with data engineers, architects, data scientists, and business stakeholders to deliver scalable, reliable, and value-driven data solutions in complex client environments.</p>\n<p><strong>Responsibilities</strong></p>\n<ul>\n<li>Apply data science and machine learning techniques to real-world business problems</li>\n<li>Work with structured and semi-structured data in data lakes, lakehouses, and data warehouses</li>\n<li>Develop and optimise data transformations for analytical and machine learning workloads</li>\n<li>Support the productionisation of data and ML solutions, including monitoring and optimisation</li>\n</ul>\n<p><strong>Requirements</strong></p>\n<ul>\n<li>3–5 years of experience in data engineering, data science, or analytics</li>\n<li>Hands-on experience delivering data and analytics solutions in project-based or client environments</li>\n<li>Strong problem-solving skills and a pragmatic, delivery-oriented mindset</li>\n</ul>\n<p><strong>Data Engineering Foundations</strong></p>\n<ul>\n<li>Experience building end-to-end data pipelines (ingestion, transformation, storage)</li>\n<li>Solid understanding of data modelling, data transformations, and feature engineering</li>\n<li>Familiarity with cloud-based data platforms, such as Azure, AWS, or GCP</li>\n</ul>\n<p><strong>Applied Data Science &amp; Analytics</strong></p>\n<ul>\n<li>Experience applying statistical analysis and machine learning techniques</li>\n<li>Strong programming skills in Python</li>\n<li>Very good SQL skills and experience working with relational databases</li>\n</ul>\n<p><strong>Nice to have</strong></p>\n<ul>\n<li>Experience with streaming technologies (e.g. Kafka, Azure Event Hubs)</li>\n<li>Exposure to GenAI, NLP, time series, or advanced analytics use cases</li>\n<li>Experience with NoSQL databases (e.g. MongoDB, Cosmos DB)</li>\n</ul>\n<p><strong>Language &amp; Mobility</strong></p>\n<ul>\n<li>Very good English skills</li>\n<li>Willingness to travel for project-related work</li>\n</ul>\n<p><strong>Benefits</strong></p>\n<p>Join our growing Data &amp; Analytics practice and make a difference. In this practice you will be utilizing the most innovative technological solutions in modern data ecosystem. In this role you’ll be able to see your own ideas transform into breakthrough results in the areas of Data &amp; Analytics strategy, Data Management &amp; Governance, Data Platforms &amp; Engineering, Analytics &amp; Data Science.</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_8b0e9386-fa9","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Infosys Consulting - Europe","sameAs":"https://jobs.workable.com","logo":"https://logos.yubhub.co/view.com.png"},"x-apply-url":"https://jobs.workable.com/view/43f8dm12rcrpZUsa228TbZ/data-engineering-%26-data-science-consultant-in-london-at-infosys-consulting---europe","x-work-arrangement":"onsite","x-experience-level":"mid","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["data science","machine learning","data engineering","cloud-based data platforms","data modelling","data transformations","feature engineering","Python","SQL","relational databases"],"x-skills-preferred":["streaming technologies","GenAI","NLP","time series","advanced analytics","NoSQL databases"],"datePosted":"2026-03-09T16:58:07.007Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"data science, machine learning, data engineering, cloud-based data platforms, data modelling, data transformations, feature engineering, Python, SQL, relational databases, streaming technologies, GenAI, NLP, time series, advanced analytics, NoSQL databases"}]}