OpenAI’s Statsig team is looking for a Machine Learning Engineer to set the technical direction for ML-powered experimentation, forecasting, and evidence generation. The role partners with product and research to build end-to-end production systems that turn experimentation data into traceable, calibrated, privacy-protected insights that support safer decision-making.
Responsibilities
- Own and execute the technical roadmap for Generative Insights and Predictive Experimentation, moving from early prototypes to production adoption.
- Build cross-experiment learning systems that retrieve and synthesize historical experiments, detect recurring effects and segment behavior, and reanalyze prior results when data or methods improve, producing hypotheses with clear evidence and provenance.
- Develop predictive models and simulation workflows, including simulation-based evaluation, to estimate likely impact, affected segments, regression risk, and uncertainty before launching a full live experiment.
- Create high-quality datasets and feature or retrieval pipelines from exposures, events, metrics, experiment metadata, and replay data, ensuring strong 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 closely with data science and product teams on experiment design, causal inference, sequential decision-making, variance reduction, and defining the boundary between prediction and causal evidence.
- Build reliable services and clear internal workflows so teams can understand and use sophisticated ML capabilities for high-stakes product decisions.
- Provide technical leadership across engineering.
Requirements
- Experience leading ambiguous 0-to-1 production ML products where outcomes were measured by better real-world decisions, not only offline metrics.
- Hands-on experience across the ML lifecycle: dataset design, training or adaptation, evaluation, deployment, monitoring, and iteration.
- Depth in one or more areas, such as LLMs and retrieval systems, ranking or recommendation, forecasting or anomaly detection, causal ML or experiment analysis, or simulation.
- Strong software engineering fundamentals and ability to build production-quality systems in Python while working comfortably across data boundaries.
- Strong grounding in machine learning study or equivalent practical experience.
- Understanding of experimentation and statistical reasoning, including why predictive accuracy does not equate to causal validity.
- Ability to treat calibration, uncertainty, provenance, privacy, and human review as product requirements.
- Capability to translate ambiguous partner questions into product and technical roadmaps, collaborating effectively with product, data science, research, and infrastructure partners.
- Interest in building for internal power users and agents, making sophisticated ML capabilities clear and actionable.
- Preference for in-person collaboration and interest in helping shape a growing Bellevue-based team.
Technologies
- Python
- LLM
- retrieval systems
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 started as an independent company focused on helping builders move faster through trustworthy experimentation and feature management. Today, the team supports initiatives across ChatGPT, Codex, model measurement, consumer experiences, business subscriptions, developer products, and shared infrastructure that connects them. Teams use Statsig to safely introduce new capabilities, compare product and model behavior, measure impact, and roll changes forward or back with confidence. 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.
About the Role
This is an end-to-end, 0-to-1 role focused on building production systems that learn from privacy-protected product and experimentation data to produce evidence-backed insights and guide which ideas are worth testing live. 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. Live experiments remain the source of causal validation. Responsibilities include designing systems that make uncertainty explicit, backtest against historical outcomes, compare predictions with online results, learn from misses, and abstain when evidence is weak. As automation becomes more powerful, the role also requires preserving clear review, permission, and approval boundaries. Collaboration includes working closely with teams building ChatGPT capabilities that can support the full company.
Location and Workplace
- Location: Seattle, WA (onsite)
- The team works in person and uses that time to move quickly, solve ambiguous problems together, and stay close to the product teams they support.
Compensation
$437,000 - $485,000 per year + Offers Equity. The base pay offered may vary depending on multiple individualized factors and related bonus(es) for eligible employees, along with the benefits listed below.
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, multiple paid coordinated company 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 also be provided
