Description
Compensation
The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The salary range for this role is $381K – $423K, which includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.
Benefits include:
- 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
- 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
About the team
The Growth team drives user and revenue growth across ChatGPT’s consumer and business segments and other OpenAI products worldwide. We work across the full funnel,from awareness and acquisition through activation, retention, and expansion,using experimentation, data science, machine learning, and product engineering to help more people discover and realize durable value from OpenAI’s products.
About this role:
We are looking for a Machine Learning Engineer to build the models and decision systems that optimize paid marketing investment for ChatGPT and other OpenAI products. You will develop budget allocation and control algorithms that determine where to invest, how quickly to spend, and how to adjust as performance changes.
Responsibilities
- Build budget allocation algorithms. Decide how to distribute investment across channels, campaigns, and markets using estimates of marginal returns, while accounting for diminishing returns, uncertainty, and business constraints.
- Develop spend pacing and adaptive control systems. Adjust budgets and spending rates as performance changes, balancing responsiveness with stability when feedback is delayed or noisy.
- Model marketing response and user value. Develop forecasts and models of conversion, long-term value, and saturation that help determine where the next dollar can have the greatest impact.
- Evaluate decisions rigorously. Use simulations, backtesting, and online experiments to test allocation and control policies.
- Own reliable production systems. Build data pipelines, training and inference workflows, and integrations that turn model outputs into budget decisions.
- Turn business goals into optimization problems. Partner with Growth Marketing, Data Science, Engineering, and Finance to define objectives and constraints, communicate tradeoffs, and use evidence to improve investment decisions.
- Help build a diverse and inclusive culture where people feel welcome, challenge assumptions, and collaborate with candor and respect.
Requirements
- 5+ years of experience as a Machine Learning Engineer, Applied Scientist, Data Scientist, or in a comparable quantitative engineering role
- Experience building and operating production ML or quantitative decision systems
- Strong software engineering skills, proficiency in Python and SQL, and hands-on experience with ML frameworks and large-scale data systems
- Strong foundations in machine learning and statistics, with experience applying optimization or control methods to decisions under uncertainty
- Experience evaluating models or algorithms with noisy, delayed, or incomplete data
- The ability to turn ambiguous goals into focused experiments, interpret mixed results, and communicate clear decisions with technical and non-technical partners
- A collaborative working style and a commitment to deploying AI responsibly