--- title: 'Machine Learning Engineer, Core Experimentation at OpenAI' canonical: 'https://feeny.ai/job/machine-learning-engineer-core-experimentation-openai-seattle-z7anbfhfdma4' type: 'job' last_seen: '2026-11-05' --- # Machine Learning Engineer, Core Experimentation at OpenAI - **Company:** [OpenAI](https://feeny.ai/companies/openai) - **Location:** Seattle, WA - **Compensation:** $437k–$485k - **Employment:** full-time - **Work type:** hybrid - **Posted:** 2026-09-24 - **Last confirmed live:** 2026-11-05 - **Apply:** https://jobs.ashbyhq.com/openai/9d4d2727-27f3-4a63-857c-a96466130645 ## Job description ## About the Team The Statsig team within OpenAI builds the experimentation, feature rollout, dynamic configuration, and analytics systems that help OpenAI ship products with speed, safety, and evidence. Our work sits on the critical path for how product, engineering, research, and go-to-market teams learn from real-world usage and make high-confidence decisions. Statsig began as an independent company focused on helping builders move faster through trustworthy experimentation and feature management. After Statsig joined OpenAI, the team began the next chapter: bringing that deep product expertise, customer intuition, and mature platform infrastructure into OpenAI as the experimentation and rollout platform for every product we ship. Today, we support teams across ChatGPT, Codex, model measurement, consumer experiences, business subscriptions, developer products, and the shared infrastructure that connects them. These teams rely on Statsig to safely introduce new capabilities, compare product and model behavior, measure impact, and roll changes forward or back with confidence. We are at a defining moment in the platform journey. OpenAI has the data, product surface area, and pace of innovation to learn faster than almost any organization in the world, but that potential only becomes real if teams can experiment responsibly, measure clearly, and roll out changes safely. We are evolving experimentation systems to help teams learn from product behavior and make better evidence-based decisions. Based out of OpenAI’s Bellevue office, we are a close-knit team that values in-person collaboration, urgency, craft, and impact. We build for other builders, and the best version of this team is one where every OpenAI product team can move faster because the experimentation and rollout layer is dependable, fast, and easy to use. ## About the Role We are looking for a Machine Learning Engineer to lead the technical direction for ML-powered experimentation and insights capabilities. You will build production systems that learn from privacy-protected product and experimentation data to generate evidence-backed insights and support decision-making, and help teams decide which ideas are worth testing live. This is an end-to-end, 0-to-1 role. You will work across ML modeling, retrieval and LLM systems, statistical methods, simulation, data and training pipelines, backend services, and user- and agent-facing product experiences. The hard part is not merely producing a plausible answer. It is making each insight and prediction traceable, calibrated, useful, and safe enough to influence real product decisions. Live experiments remain the source of causal validation. You will design systems that make uncertainty explicit, backtest against historical outcomes, compare predictions with online results, learn from misses, and abstain when the evidence is weak. You will preserve clear review, permission, and approval boundaries as automation becomes more powerful. You will collaborate closely with teams building ChatGPT, Codex, model measurement workflows, consumer products, Growth, business subscription experiences, developer products, and shared infrastructure. You will turn their most important learning and decision problems into general platform capabilities that can support the full company. In This Role, You Will - Set and execute the technical roadmap for Generative Insights and Predictive Experimentation, from early prototypes through production adoption. - Build cross-experiment learning systems that retrieve and synthesize historical experiments, detect recurring effects and segment behavior, reanalyze prior results when data or methods improve, and generate hypotheses with clear evidence and provenance. - Develop predictive models and simulation workflows, including simulation-based evaluation approaches, to estimate likely impact, affected segments, regression risk, and uncertainty before a full live experiment. - Create high-quality datasets and feature or retrieval pipelines from exposures, events, metrics, experiment metadata, and replay data, with strong lineage, freshness, privacy, and data-quality controls. - Establish rigorous evaluation through offline benchmarks, backtests, calibration, drift monitoring, prediction-to-outcome comparisons, and explicit failure or abstention behavior. - Turn models into durable product, API, and agent workflows that move from an insight to experiment design, approval-gated action, and measured learning. - Partner deeply with data science and product teams on experiment design, causal inference, sequential decision-making, variance reduction, and the boundary between prediction and causal evidence. - Build reliable services and intuitive workflows so sophisticated ML capabilities are understandable and useful to teams making high-stakes product decisions. - Provide technical leadership across engineering, product, data science, and research partners, and raise the bar for production ML quality across the platform. You Might Thrive In This Role If You - Have led ambiguous 0-to-1 production ML products where success was measured by better real-world decisions, not only offline model metrics. - Have strong hands-on experience across the ML lifecycle: dataset design, training or adaptation, evaluation, deployment, monitoring, and iteration. - Bring depth in one or more of LLM and retrieval systems, ranking or recommendation, forecasting or anomaly detection, causal ML or experiment analysis, or simulation. You do not need to have done all of them. - Have strong software engineering fundamentals and can build high-quality production systems in Python while working comfortably across data, backend, and platform boundaries. - Have a strong grounding in machine learning, statistics, computer science, or a related field through formal study or equivalent practical experience. - Understand experimentation and statistical reasoning, especially why predictive accuracy is not the same as causal validity. - Treat calibration, uncertainty, provenance, privacy, and human review as product requirements, not cleanup work. - Can translate ambiguous partner questions into a product and technical roadmap, and work well with product, data science, research, and infrastructure partners. - Enjoy building for internal power users and agents, and can make sophisticated ML capabilities feel clear and actionable. - Value in-person collaboration and want to help shape a growing Bellevue-based team. Location and Workplace This role is based in Bellevue, Washington. The team works in person and uses that time to move quickly, solve ambiguous problems together, and stay close to the product teams we support. ## 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. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see [OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement](https://cdn.openai.com/policies/eeo-policy-statement.pdf). Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through [this form](https://form.asana.com/?d=57018692298241&k=5MqR40fZd7jlxVUh5J-UeA). No response will be provided to inquiries unrelated to job posting compliance. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this [link](https://form.asana.com/?k=bQ7w9h3iexRlicUdWRiwvg&d=57018692298241). [OpenAI Global Applicant Privacy Policy](https://cdn.openai.com/policies/global-employee-and-contractor-privacy-policy.pdf) At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology. ## About OpenAI ## Company Overview - **One-liner**: OpenAI is an AI research and deployment company with a mission to ensure that artificial general intelligence (AGI) benefits all of humanity. - **Entity Type**: Private – structured as a nonprofit parent (OpenAI Foundation) governing a for-profit public benefit corporation (OpenAI Group). The company has confidentially submitted a draft S-1 registration statement to the SEC, signaling a potential future IPO. - **Headquarters**: San Francisco, California, USA - **Founded**: 2015 - **Founders**: Sam Altman, Greg Brockman, Ilya Sutskever, John Schulman, Wojciech Zaremba, and others ## Core Business - **Primary industry**: Artificial intelligence research and deployment - **Target customers**: B2B (enterprises via API and platform), B2C (ChatGPT users), and developers (API, open-source models) - **Mission**: To ensure that artificial general intelligence (AGI) – AI systems that are generally smarter than humans – benefits all of humanity. ## Products & Services - **[ChatGPT](https://openai.com/chatgpt)**: Conversational AI assistant available via web and mobile, used by consumers and integrated into enterprise workflows. - **[GPT-4 API](https://openai.com/product)**: Large language model accessed via API for developers to build AI-powered applications (text generation, summarization, code assistance, etc.). - **[DALL-E](https://openai.com/dall-e-3)**: Image generation model that creates images from natural language descriptions. - **[Whisper](https://openai.com/research/whisper)**: Open-source speech recognition system. - **[OpenAI Agents](https://openai.com/)**: Emerging product category focused on autonomous AI agents that can perform tasks on behalf of users (announced June 2026). ## Market Standing - **Valuation**: Not publicly disclosed in the provided sources. Widely reported in the press at ~$300 billion as of early 2026 (post-funding round), but this figure is not confirmed by OpenAI’s official materials. - **Key Metric**: Total funding exceeds $13 billion from Microsoft and other investors. The company has also confidentially filed a draft S-1 with the SEC (see [openai.com](https://openai.com/) – “Confidential submission of draft S-1”). - **Notable Investors/Partners**: Microsoft (strategic partner and largest investor), with additional participation from Thrive Capital, Sequoia Capital, Andreessen Horowitz, and others. - **Growth Signals**: Active hiring with 721 open roles posted; expansion into agent-based products; confidential IPO filing; continued deployment of frontier models. ## Competitive Advantages - **First-mover & brand recognition**: ChatGPT became the fastest-growing consumer app in history, establishing OpenAI as the household name in generative AI. - **Research leadership**: Consistently publishes cutting-edge research (GPT-4, DALL-E 3, Whisper, etc.) and attracts top AI talent. - **Compute & infrastructure**: Exclusive partnership with Microsoft provides massive cloud computing resources (Azure) for training and inference. - **Unique governance structure**: The nonprofit parent ensures mission alignment (AGI benefiting humanity) while the for-profit arm can raise capital and scale products. ## Strategic Focus - **Safe AGI development**: Continued emphasis on safety research, alignment, and responsible deployment (e.g., safety systems, model provenance). - **Enterprise expansion**: Growing ChatGPT Enterprise and API offerings for businesses. - **Agentic AI**: Building autonomous agents that can “transform work” (announced June 2026). - **IPO preparation**: Confidential S-1 filing indicates intent to go public, which will require increased financial transparency and governance maturity. ## Why Work Here - **Culture & values**: OpenAI’s stated values include “Humanity first,” “Act with humility,” “Feel the AGI,” and “Ship joy.” Operating principles emphasize agency, creativity, rapid iteration, and intense focus. - **Benefits**: Competitive health insurance, mental healthcare support, 24-week paid birth-parent leave (plus 20 weeks for other parents), generous PTO, annual learning & development stipend, daily meals (breakfast, lunch, dinner), and a domestic conference budget. - **Work environment**: Hybrid/office-based (San Francisco headquarters). The company offers a residency program for researchers and engineers transitioning into AI. - **Impact**: Employees work directly on frontier AI models that shape the industry and society. The mission-driven nature attracts talent passionate about AGI’s societal implications. ## Sources 1. [OpenAI About Page](https://openai.com/about/) 2. [OpenAI Careers Page](https://openai.com/careers/) 3. [OpenAI Job Search](https://openai.com/careers/search) 4. 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