5 days ago
Base Salary
$437k - $485k/yr
Responsibilities
- Set and execute the technical roadmap for Generative Insights and Predictive Experimentation from prototypes through production adoption.
- Build cross-experiment learning systems that retrieve and synthesize historical experiments, identify recurring effects and segment behavior, reanalyze prior results, and generate evidence-backed hypotheses.
- Develop predictive models and simulation workflows to estimate impact, affected segments, regression risk, and uncertainty before live experiments.
- Create datasets and feature or retrieval pipelines from exposures, events, metrics, experiment metadata, and replay data with lineage, freshness, privacy, and data-quality controls.
- Establish evaluation through offline benchmarks, backtests, calibration, drift monitoring, prediction-to-outcome comparisons, and explicit failure or abstention behavior.
- Turn models into durable products, APIs, and agent workflows supporting experiment design, approval-gated action, and measured learning.
- Partner with data science and product teams on experiment design, causal inference, sequential decision-making, variance reduction, and predictive versus causal evidence.
- Build reliable services and intuitive workflows for teams making high-stakes product decisions.
- Provide technical leadership across engineering, product, data science, and research partners.
Requirements
- Experience leading ambiguous 0-to-1 production ML products measured by real-world decision quality.
- Hands-on experience across dataset design, model training or adaptation, evaluation, deployment, monitoring, and iteration.
- Depth in one or more of LLM and retrieval systems, ranking or recommendation, forecasting or anomaly detection, causal ML or experiment analysis, or simulation.
- Strong software engineering fundamentals and the ability to build production systems in Python across data, backend, and platform boundaries.
- Grounding in machine learning, statistics, computer science, or a related field through formal study or equivalent practical experience.
- Understanding of experimentation and statistical reasoning, including the distinction between predictive accuracy and causal validity.
- Experience treating calibration, uncertainty, provenance, privacy, and human review as product requirements.
- Ability to translate ambiguous partner questions into product and technical roadmaps and collaborate with product, data science, research, and infrastructure teams.
- Interest in building for internal power users and agents and making sophisticated ML capabilities clear and actionable.
Benefits
- The role is based in Bellevue, Washington, with in-person team collaboration.
- The position offers the opportunity to shape a growing Bellevue-based team and build experimentation capabilities used across OpenAI products.
Tech Stack
Categories
About OpenAI
OpenAI builds and deploys large-scale AI models and tools—including ChatGPT, GPT-4–class models, DALL·E, and Whisper—sold via APIs and enterprise subscriptions to developers and businesses. It monetizes through usage-based API pricing and ChatGPT Plus/Team/Enterprise, and also reaches customers via Microsoft’s Azure OpenAI Service. Founded in 2015 and headquartered in San Francisco, it operates as a private partnership.