Description
Compensation
$437K – $485K • Offers Equity
The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.
- Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
- Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
- 401(k) retirement plan with employer match
- Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
- Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
- 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)
- Mental health and wellness support
- Employer-paid basic life and disability coverage
- Annual learning and development stipend to fuel your professional growth
- Daily meals in our offices, and meal delivery credits as eligible
- Relocation support for eligible employees
- Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.
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.
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.
Responsibilities
- 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.
Requirements
- 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.