Machine Learning Engineer, Core Experimentation
Confirmed on the employer's own hiring board on Sep 24, 2026. First seen by Alion on Sep 24, 2026. OpenAI scores B on the Alion truth index.
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.
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.
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