Join OpenAI’s Statsig team to build ML-powered experimentation and insights that help product teams make safer, evidence-backed decisions. You’ll lead the technical direction for systems that turn experimentation data into traceable, calibrated, privacy-protected evidence, while keeping uncertainty and failure modes explicit throughout the workflow.
This Machine Learning Engineer, Core Experimentation role is based in Seattle, WA (onsite) and is an end-to-end, 0-to-1 opportunity. You’ll help take early ideas from prototype to production adoption, and support the critical path where engineering, research, and go-to-market teams learn from real-world usage and make high-confidence decisions.
What you’ll do
- Own the technical roadmap for Generative Insights and Predictive Experimentation, progressing from early prototypes through production adoption.
- Build cross-experiment learning systems that retrieve and synthesize historical experiments, detect recurring effects and segment behavior, and reanalyze prior results as data or methods improve.
- Generate hypotheses with clear evidence and provenance, supported by robust learning from past results.
- Develop predictive models and simulation-based evaluation workflows to estimate likely impact, affected segments, regression risk, and uncertainty prior to live experimentation.
- Create high-quality datasets and feature or retrieval pipelines spanning exposures, events, metrics, experiment metadata, and replay data, with lineage, freshness, privacy, and data-quality controls.
- Establish rigorous evaluation using 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 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.
- Provide reliable services and intuitive workflows so sophisticated ML capabilities are understandable and useful for high-stakes product decisions.
- Deliver technical leadership across engineering.
What you bring
- Experience leading ambiguous 0-to-1 production ML products where success is judged by better real-world decisions, not only offline metrics.
- Hands-on expertise across the ML lifecycle: dataset design, training or adaptation, evaluation, deployment, monitoring, and iteration.
- Depth in one or more areas: LLMs and retrieval systems, ranking/recommendation, forecasting or anomaly detection, causal ML or experiment analysis, or simulation.
- Strong software engineering fundamentals and the ability to build high-quality production systems in Python while working comfortably across data boundaries.
- Strong grounding in machine learning (or equivalent practical experience).
- Understanding of experimentation and statistical reasoning, including why predictive accuracy does not equal causal validity.
- Ability to treat calibration, uncertainty, provenance, privacy, and human review as product requirements.
- Capacity to translate ambiguous partner questions into a product and technical roadmap, collaborating effectively with product, data science, research, and infrastructure partners.
- Enjoyment building for internal power users and agents, making sophisticated ML capabilities clear and actionable.
- Value in-person collaboration and interest in helping shape a growing Bellevue-based team.
Tools you’ll use
Python, LLM, retrieval systems
Compensation
$437,000 - $485,000 per year + Offers Equity. The base pay offered may vary depending on multiple individualized factors, and eligible employees may receive related compensation and benefits.
Benefits at OpenAI
- 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, multiple paid coordinated office closures 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
- Daily meals in 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 be provided
About the team
The Statsig team within OpenAI builds experimentation, feature rollout, dynamic configuration, and analytics systems that help OpenAI ship products with speed, safety, and evidence. Statsig began as an independent company focused on trustworthy experimentation and feature management, and today supports teams across ChatGPT, Codex, model measurement, consumer experiences, business subscriptions, developer products, and shared infrastructure.
Live experiments remain the source of causal validation, and this role 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.
