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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_fe389037-d6e"},"title":"Product Management IC5","description":"<p>As a Principal Product Manager on the MAI Suggestions team, you will lead a significant product area within MAI Suggestions, initially focused on Experiences and Growth. 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You will work closely with engineering, design, data science, and partner teams to define product direction, lead complex initiatives, and deliver step-function improvements in engagement, adoption, and task success across MAI surfaces such as Search, Browsing, and Copilot experiences.</p>\n<p>Responsibilities:</p>\n<ul>\n<li>Own product direction and outcomes for a major area within MAI Suggestions, initially focused on Experiences and Growth—from problem framing through execution to measurable customer and business impact.</li>\n<li>Set and sustain a high-velocity product operating rhythm by creating clarity, generating energy, and reducing friction—accelerating decision-making and setting a high bar for pace, ownership, and execution across cross-functional teams.</li>\n<li>Enable rapid shipping, learning, and iteration by leveraging AI-powered tooling and tight cross-functional collaboration to compress delivery cycles, increase experimentation throughput, and deliver impact ahead of the curve.</li>\n<li>Identify and lead high-impact opportunities to improve user engagement, activation, retention, and task success using data, experimentation, and deep customer insights.</li>\n<li>Frame ambiguous problem spaces effectively, define clear success metrics, and translate insights into well-prioritized product bets and roadmaps.</li>\n<li>Lead cross-team and cross-org initiatives, influencing partner teams to deliver cohesive, high-quality experiences across MAI surfaces.</li>\n<li>Partner closely with engineering and data science to make informed trade-offs across UX quality, velocity, scalability, and model performance.</li>\n<li>Define and evolve metrics, evaluation frameworks, and experiments to assess experience quality, growth impact, and long-term value.</li>\n<li>Act as a product thought leader, shaping principles, best practices, and reusable approaches that scale beyond a single problem area.</li>\n<li>Communicate product strategy, trade-offs, and outcomes to senior stakeholders, driving alignment and informed decision-making.</li>\n<li>Mentor and raise the bar for other PMs through design reviews, strategic guidance, and shared ownership of product excellence.</li>\n</ul>\n<p>Qualifications:</p>\n<ul>\n<li>Bachelor’s Degree AND 8+ years experience in product/service/program management or software development. 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We partner with Product, Engineering, Risk, Finance, and Go-to-Market to make paying for OpenAI products seamless, reliable, and efficient worldwide.</p>\n<p><strong>About the Role</strong></p>\n<p>As a Data Scientist on FinEng, you’ll own the analytics and experimentation that improve our <strong>checkout and payments</strong>, <strong>subscriptions</strong>, and <strong>pricing &amp; monetization</strong> systems. You’ll define the metrics that matter, build the source-of-truth data assets, and design experiments that increase conversion, reduce churn and payment failures, and expand global payment method coverage. Your work will directly influence revenue, customer experience, and how we scale internationally.</p>\n<p>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.</p>\n<p><strong>In this role, you will</strong></p>\n<ul>\n<li>Own checkout &amp; payments analytics and experimentation across methods and locales (e.g., bank transfers, emerging rails), improving conversion while monitoring risk and latency.</li>\n</ul>\n<ul>\n<li>Build and run the experimentation program for in-house checkout—define success metrics and guardrails, execute staged rollouts, and use offline incrementality when online tests aren’t feasible.</li>\n</ul>\n<ul>\n<li>Create operational visibility and source-of-truth data with FinEng Data Engineering—land team-level metrics, SLAs, and self-serve dashboards that drive proactive action.</li>\n</ul>\n<ul>\n<li>Lead subscription, retention, and monetization analytics—ship launch-readiness for new subscription features, reduce involuntary churn (e.g., targeted retrials/nudges), and develop elasticity/FX frameworks toward pricing optimality.</li>\n</ul>\n<p><strong>You might thrive in this role if you have</strong></p>\n<ul>\n<li>5+ years in a quantitative role (data science, product analytics, or experimentation) in high-growth or fintech environments</li>\n</ul>\n<ul>\n<li>Fluency in <strong>SQL</strong> and <strong>Python</strong>, with a track record designing and interpreting A/B tests and quasi-experiments</li>\n</ul>\n<ul>\n<li>Experience building product metrics from scratch and operationalizing them for decision-making</li>\n</ul>\n<ul>\n<li>Excellent communication skills with PMs, engineers, risk/finance partners, and executives</li>\n</ul>\n<ul>\n<li>Strategic instincts beyond significance tests—clear thinking about tradeoffs (conversion vs. risk vs. cost vs. user experience)</li>\n</ul>\n<p><strong>You could be an especially great fit if you have</strong></p>\n<ul>\n<li>Payments, checkout, or subscription analytics experience (PSPs, bank rails, disputes/refunds, risk, e-commerce)</li>\n</ul>\n<ul>\n<li>Background in <strong>offline incrementality</strong> methods, uplift modeling, CUPED/causal inference, or counterfactual evaluation</li>\n</ul>\n<ul>\n<li>Experience with internationalization/local payments, FX, and pricing &amp; packaging strategy</li>\n</ul>\n<ul>\n<li>Comfort building operational analytics (alerting, SLIs/SLOs) and partnering closely with data engineering</li>\n</ul>\n<p><strong>Benefits</strong></p>\n<ul>\n<li>Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts</li>\n</ul>\n<ul>\n<li>Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)</li>\n</ul>\n<ul>\n<li>401(k) retirement plan with employer match</li>\n</ul>\n<ul>\n<li>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)</li>\n</ul>\n<ul>\n<li>Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees</li>\n</ul>\n<ul>\n<li>13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick and safe time (1 hour per 30 hours worked)</li>\n</ul>\n<ul>\n<li>Mental health and wellness support</li>\n</ul>\n<ul>\n<li>Employer-paid basic life and disability coverage</li>\n</ul>\n<ul>\n<li>Annual learning and development stipend to fuel your professional growth</li>\n</ul>\n<ul>\n<li>Daily meals in our offices, and meal delivery credits as eligible</li>\n</ul>\n<ul>\n<li>Relocation support for eligible employees</li>\n</ul>\n<ul>\n<li>Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.</li>\n</ul>\n<p><strong>Salary</strong></p>\n<p>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 for eligible employees and benefits.</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_4d4df1fe-7ee","directApply":true,"hiringOrganization":{"@type":"Organization","name":"OpenAI","sameAs":"https://jobs.ashbyhq.com","logo":"https://logos.yubhub.co/openai.com.png"},"x-apply-url":"https://jobs.ashbyhq.com/openai/898a87fb-4cb8-450e-9840-ee5dc710a57d","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$230K – $385K","x-skills-required":["SQL","Python","A/B testing","quasi-experiments","product metrics","operational analytics"],"x-skills-preferred":["payments","checkout","subscription analytics","offline incrementality","uplift modeling","CUPED/causal inference","counterfactual evaluation","internationalization/local payments","FX","pricing & packaging strategy"],"datePosted":"2026-03-06T18:32:44.006Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"SQL, Python, A/B testing, quasi-experiments, product metrics, operational analytics, payments, checkout, subscription analytics, offline incrementality, uplift modeling, CUPED/causal inference, counterfactual evaluation, internationalization/local payments, FX, pricing & packaging strategy","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":230000,"maxValue":385000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_c66d7006-392"},"title":"Data Scientist, B2B","description":"<p><strong>Data Scientist, B2B</strong></p>\n<p><strong>Location</strong></p>\n<p>San Francisco; New York City</p>\n<p><strong>Employment Type</strong></p>\n<p>Full time</p>\n<p><strong>Location Type</strong></p>\n<p>On-site</p>\n<p><strong>Department</strong></p>\n<p>Data Science</p>\n<p><strong>Compensation</strong></p>\n<ul>\n<li>$293K – $515K • Offers Equity</li>\n</ul>\n<p>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.</p>\n<ul>\n<li>Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts</li>\n</ul>\n<ul>\n<li>Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)</li>\n</ul>\n<ul>\n<li>401(k) retirement plan with employer match</li>\n</ul>\n<ul>\n<li>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)</li>\n</ul>\n<ul>\n<li>Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees</li>\n</ul>\n<ul>\n<li>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)</li>\n</ul>\n<ul>\n<li>Mental health and wellness support</li>\n</ul>\n<ul>\n<li>Employer-paid basic life and disability coverage</li>\n</ul>\n<ul>\n<li>Annual learning and development stipend to fuel your professional growth</li>\n</ul>\n<ul>\n<li>Daily meals in our offices, and meal delivery credits as eligible</li>\n</ul>\n<ul>\n<li>Relocation support for eligible employees</li>\n</ul>\n<ul>\n<li>Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.</li>\n</ul>\n<p>More details about our benefits are available to candidates during the hiring process.</p>\n<p>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.</p>\n<p><strong>About the Team</strong></p>\n<p>Our B2B product organisation is building the systems that will accelerate and automate how companies operate using AI coworkers. Our vision is to enable companies to scale to $1B+ with teams of fewer than five people by leveraging AI agents and automation. We are building the infrastructure, products, and analytics that allow organisations to deploy fleets of AI agents, automate workflows, and dramatically increase employee productivity. This team sits at the centre of the future of work: defining how humans collaborate with AI systems across companies, workflows, and knowledge.</p>\n<p><strong>About the Role</strong></p>\n<p>We are looking for a product-focused Data Scientist to help shape the next generation of AI-powered enterprise products, including ChatGPT Enterprise and agentic automation systems.</p>\n<p>This is not a traditional analytics role. You will work directly with product managers, engineers, and researchers to define how AI agents behave, how organisations adopt them, and how we measure their impact on work.</p>\n<p>You’ll analyse how users interact with AI systems, inform product strategy, and help build the measurement frameworks that guide the future of enterprise AI.</p>\n<p><strong>In this role, you will:</strong></p>\n<ul>\n<li>Partner closely with product, engineering, and research teams to shape AI-powered enterprise products</li>\n</ul>\n<ul>\n<li>Define metrics and measurement frameworks for AI agents, automation workflows, and enterprise adoption</li>\n</ul>\n<ul>\n<li>Analyse how organisations use AI tools and identify opportunities to improve product experience and business impact</li>\n</ul>\n<ul>\n<li>Drive insights around enterprise adoption, retention, and value realisation</li>\n</ul>\n<ul>\n<li>Inform product decisions around agent orchestration, enterprise workflows, and AI productivity tools</li>\n</ul>\n<ul>\n<li>Design experiments and evaluations that improve AI system performance and user outcomes</li>\n</ul>\n<ul>\n<li>Help define how companies operate with AI coworkers and automated workflows</li>\n</ul>\n<p><strong>What We’re Looking For</strong></p>\n<p><strong>Must Have:</strong></p>\n<ul>\n<li>Strong product analytics or data science experience</li>\n</ul>\n<ul>\n<li>Deep curiosity about AI systems and how they change how work gets done</li>\n</ul>\n<ul>\n<li>Ability to work closely with product and engineering teams in 0→1 product environments</li>\n</ul>\n<ul>\n<li>Strong analytical skills (experimentation, behavioural analysis, product metrics)</li>\n</ul>\n<ul>\n<li>Builder mindset — someone who wants to shape products, not just analyse them</li>\n</ul>\n<p><strong>Nice to Have:</strong></p>\n<ul>\n<li>Experience with B2B SaaS or enterprise analytics</li>\n</ul>\n<ul>\n<li>Experience with developer platforms, APIs, or technical products</li>\n</ul>\n<ul>\n<li>Background in AI/ML systems, agent frameworks, or LLM-based products</li>\n</ul>\n<ul>\n<li>Systems thinking around how complex tools and workflows operate at scale</li>\n</ul>\n<p><strong>Who Thrives Here:</strong></p>\n<ul>\n<li>AI-native — actively experimenting with and building with AI tools</li>\n</ul>\n<ul>\n<li>Product thinkers who want to shape the future of work</li>\n</ul>\n<ul>\n<li>Builders who enjoy early-stage ambiguity and 0→1 problems</li>\n</ul>\n<ul>\n<li>Operators who can work independently and drive large parts of a product area</li>\n</ul>\n<p><strong>Impact</strong></p>\n<p><strong>You will help define:</strong></p>\n<ul>\n<li>How organisations deploy and manage AI agents</li>\n</ul>\n<ul>\n<li>How AI transforms enterprise productivity</li>\n</ul>\n<ul>\n<li>The measurement systems behind AI-native companies</li>\n</ul>\n<ul>\n<li>This is an opportunity to help create the “Codex moment” for non-coding work — defining how businesses operate in an AI-first world.</li>\n</ul>\n<p><strong>About OpenAI</strong></p>\n<p>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. 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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 for eligible employees and benefits.</p>\n<ul>\n<li>Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts</li>\n</ul>\n<ul>\n<li>Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)</li>\n</ul>\n<ul>\n<li>401(k) retirement plan with employer match</li>\n</ul>\n<ul>\n<li>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)</li>\n</ul>\n<ul>\n<li>Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees</li>\n</ul>\n<ul>\n<li>13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick and safe time (1 hour per 30 hours worked)</li>\n</ul>\n<ul>\n<li>Mental health and wellness support</li>\n</ul>\n<ul>\n<li>Employer-paid basic life and disability coverage</li>\n</ul>\n<ul>\n<li>Annual learning and development stipend to fuel your professional growth</li>\n</ul>\n<ul>\n<li>Daily meals in our offices, and meal delivery credits as eligible</li>\n</ul>\n<ul>\n<li>Relocation support for eligible employees</li>\n</ul>\n<ul>\n<li>Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.</li>\n</ul>\n<p>More details about our benefits are available to candidates during the hiring process.</p>\n<p>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.</p>\n<p><strong><strong>About the Team</strong></strong></p>\n<p><strong>Codex</strong> is OpenAI’s first-party developer product focused on agentic software engineering. We’re building tools that help engineers design, write, test, and ship code faster—safely and at scale. We partner tightly with research and product to translate model advances into tangible developer productivity.</p>\n<p><strong><strong>About the Role</strong></strong></p>\n<p>As a Data Scientist on Codex, you will measure and accelerate product-market fit for AI developer tools. You’ll define what “developer productivity” means for our product, run experiments on new coding models and UX, and pinpoint where the model helps or hurts across languages and tasks. Your insights will directly shape how an entire industry builds software.</p>\n<p>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.</p>\n<p><strong><strong>In this role, you will</strong></strong></p>\n<ul>\n<li>Embed with the Codex product team to discover opportunities that improve developer outcomes and growth</li>\n</ul>\n<ul>\n<li>Design and interpret A/B tests and staged rollouts of <strong>new coding models</strong> and product features</li>\n</ul>\n<ul>\n<li>Define and operationalize metrics such as suggestion acceptance, edit distance, compile/test pass rates, task completion, latency, and session productivity</li>\n</ul>\n<ul>\n<li>Build dashboards and analyses that help the team self-serve answers to product questions (by language, framework, repo size, task type)</li>\n</ul>\n<ul>\n<li>Diagnose failure modes and partner with Research on targeted improvements (model quality signals, user feedback, evals)</li>\n</ul>\n<p><strong><strong>You might thrive in this role if you have</strong></strong></p>\n<ul>\n<li>5+ years in a quantitative role at a developer-facing or high-growth product</li>\n</ul>\n<ul>\n<li>Fluency in SQL and Python; comfort with experiment design and causal inference</li>\n</ul>\n<ul>\n<li>Experience defining product metrics tied to user value</li>\n</ul>\n<ul>\n<li>Ability to communicate clearly with PM, Eng, and Design—and to influence product direction</li>\n</ul>\n<p><strong><strong>You could be an especially great fit if you have</strong></strong></p>\n<ul>\n<li>Strong programming background; ability to prototype, run simulations, and reason about code quality</li>\n</ul>\n<ul>\n<li>Familiarity with IDE/extension telemetry or developer tooling analytics</li>\n</ul>\n<ul>\n<li>Prior experience with NLP/LLMs, code models, or evaluations for generative coding</li>\n</ul>\n<p><strong>About OpenAI</strong></p>\n<p>OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. 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