Machine Learning Engineer, Growth
ML Engineer · Senior · Full Time
Opens OpenAI's application page
Role
What you'll do.
Join OpenAI's Growth team as a Machine Learning Engineer to build sophisticated budget allocation and adaptive control systems that optimize paid marketing investment across ChatGPT and OpenAI's product suite globally. In this role, you'll develop machine learning models and decision algorithms that intelligently allocate resources across channels and campaigns while managing performance signals that are noisy, delayed, and incomplete. You'll own the full lifecycle from problem formulation and prototyping through production deployment, experimentation, and continuous optimization, combining expertise in ML, optimization algorithms, and rigorous experimentation to drive user acquisition and revenue growth.
Responsibilities
- Build Budget Allocation Algorithms: Design and implement algorithms that distribute investment optimally across marketing channels, campaigns, and geographic markets using marginal return estimates while accounting for diminishing returns, uncertainty quantification, and complex business constraints. Leverage multi-armed bandit approaches, optimization frameworks, and causal inference techniques to maximize marketing ROI.
- Develop Adaptive Spend Pacing and Control Systems: Engineer dynamic budget adjustment and spend rate control mechanisms that respond effectively to real-time performance changes while maintaining stability in delayed-feedback environments. Implement spend guardrails, continuous monitoring systems, and graceful fallback strategies to ensure reliable operation under adverse conditions.
- Model Marketing Response and User Value Prediction: Build predictive models forecasting conversion probabilities, long-term customer lifetime value, and saturation effects across channels. Use these models to quantify marginal impact and inform allocation decisions, integrating advanced statistical methods with domain knowledge from marketing and product teams.
- Conduct Rigorous Evaluation and Validation: Design and execute offline simulations, historical backtesting protocols, and online A/B experiments to validate allocation policies before production deployment. Partner with Data Science to measure incrementality through rigorous causal inference methods and ensure offline improvements translate into measurable business outcomes.
- Own Production ML Systems and Data Infrastructure: Build and maintain end-to-end production ML pipelines including data ingestion, feature engineering, model training workflows, inference systems, and budget decision integrations. Establish comprehensive data quality monitoring, model performance tracking, automated retraining schedules, and maintain rigorous privacy and data consent practices.
- Translate Business Objectives into Optimization Problems: Collaborate closely with Growth Marketing, Data Science, Engineering, and Finance teams to translate high-level business goals into precise, measurable optimization objectives and constraints. Communicate technical tradeoffs clearly to non-technical stakeholders and use empirical evidence to guide strategic investment decisions.
- Foster Inclusive Team Culture: Contribute actively to building a diverse and inclusive team environment where all members feel valued, challenge assumptions respectfully, and collaborate with transparency and mutual respect aligned with OpenAI's mission to ensure AI benefits all humanity.
Qualifications
What we look for.
Technical
Machine Learning Systems Design
Expert-level ability to architect end-to-end ML systems including data pipelines, feature engineering, model training frameworks, and inference architectures. Deep understanding of ML system reliability, serving latency requirements, and monitoring strategies for production environments handling real-time decisions.
Optimization and Control Methods
Strong theoretical foundation in optimization algorithms including convex optimization, gradient-based methods, and constrained optimization. Hands-on experience applying control theory, multi-armed bandit algorithms, reinforcement learning, or adaptive control methods to sequential decision problems under uncertainty.
Statistical Inference and Causal Analysis
Advanced knowledge of causal inference methodologies including randomized experimentation, propensity score matching, difference-in-differences analysis, and synthetic controls. Proficiency in handling causal attribution from observational data, measuring incrementality, and designing online and offline experiments with statistical rigor.
Python and Software Engineering
Production-grade Python proficiency for building robust, maintainable ML systems. Solid understanding of software engineering best practices including version control, testing strategies, code review processes, and deployment pipelines. Experience with containerization tools and CI/CD workflows.
SQL and Data Engineering
Advanced SQL expertise for complex data querying, aggregation, and analysis at scale. Practical experience with data warehouses or data lakes, understanding of query optimization, and ability to work with large-scale structured data systems supporting ML feature engineering.
ML Frameworks and Large-Scale Computing
Hands-on experience with modern ML frameworks such as PyTorch, TensorFlow, or scikit-learn. Experience working with large-scale data processing systems including Spark, distributed training frameworks, and cloud computing platforms for ML workloads.
Time Series Analysis and Forecasting
Practical experience with time series modeling, forecasting techniques, and handling temporal dependencies in data. Understanding of seasonal patterns, trend analysis, and methods for predicting performance metrics in dynamic marketing environments.
A/B Testing and Experimentation
Deep expertise in experiment design, statistical testing, sample size calculation, and sensitivity analysis. Proficiency in identifying confounds, dealing with multiple comparisons, and computing power analyses for online experiments in high-velocity product environments.
Education
Advanced Technical Foundation
Bachelor's degree in Computer Science, Engineering, Statistics, Mathematics, Physics, or related quantitative field. Advanced degree (Master's or PhD) in Machine Learning, Statistics, Operations Research, or related discipline highly preferred, or equivalent professional impact demonstrating mastery of these domains.
Core Mathematical Competency
Strong foundation in linear algebra, probability, statistics, and calculus. Demonstrated understanding of mathematical optimization principles, statistical inference, and the ability to reason rigorously about uncertainty and variance in data-driven decision systems.
Experience
Production ML System Development
Minimum 5+ years building and operating ML or quantitative decision systems in production environments. Demonstrated end-to-end ownership from initial problem framing and exploratory data analysis through model development, deployment, A/B testing, and long-term monitoring and maintenance.
Decision Systems Under Uncertainty
Proven experience developing algorithms that make reliable decisions from noisy, delayed, or incomplete data signals. Track record of balancing model sophistication with operational reliability, and judgment in evaluating when perfect model accuracy must be traded for deployed system robustness.
Marketing or Growth Analytics
Prior experience in growth analytics, marketing analytics, or ads-related machine learning. Familiarity with marketing funnel dynamics, channel performance measurement, customer acquisition cost metrics, and the challenges of optimizing under budget constraints and delayed feedback.
Cross-Functional Collaboration
Demonstrated ability to translate ambiguous business problems into focused experiments, interpret mixed or complex results, and communicate technical insights to diverse stakeholders including marketing, product, finance, and executive teams. Strong skills in influencing without authority.
Problem Scoping and Prioritization
Evidence of ability to scope ill-defined problems, identify high-leverage opportunities, and prioritize among competing technical and business objectives. Experience working in fast-moving environments with incomplete information and evolving requirements.
Skills
Required
Python
Production-grade Python programming for ML systems, data processing, and algorithm implementation. Proficiency with NumPy, Pandas, and scientific computing libraries for numerical work.
SQL
Advanced SQL for data extraction, transformation, aggregation, and analysis at scale. Capability to write optimized queries against large datasets and data warehouses.
Machine Learning Algorithms
Deep understanding of supervised and unsupervised learning algorithms, ensemble methods, and statistical modeling techniques. Ability to select appropriate algorithms for specific problems and understand their tradeoffs.
Experimentation Design
Expertise in A/B testing, online experiments, statistical hypothesis testing, and causal inference. Proficiency in experiment design, power analysis, and statistical significance assessment.
Optimization Methods
Practical knowledge of optimization algorithms, constrained optimization, and convex optimization theory. Experience applying optimization to real-world decision and allocation problems.
Model Evaluation and Validation
Expertise in assessing model performance through backtesting, simulation, cross-validation, and online evaluation. Understanding of offline-to-online gaps and validation in delayed feedback environments.
Data Pipeline Development
Capability to build and maintain robust data pipelines, feature engineering workflows, and data quality systems that feed ML models in production.
Communication and Stakeholder Management
Ability to communicate complex technical concepts clearly to non-technical audiences. Skill in translating business requirements into technical specifications and presenting findings with actionable recommendations.
Preferred
PyTorch or TensorFlow
Nice to haveHands-on experience building models with deep learning frameworks, though focus is on practical application rather than cutting-edge research.
Causal Inference Methods
Nice to haveAdvanced knowledge of causal graph theory, propensity score methods, instrumental variables, or other causal inference techniques for extracting causal insights from observational data.
Reinforcement Learning
Nice to haveExperience with reinforcement learning, multi-armed bandits, or adaptive control systems that learn and improve over time through interaction.
Time Series Forecasting
Nice to haveExperience building forecasting models for time-dependent data, understanding seasonality, trend modeling, and ARIMA or modern deep learning forecasting approaches.
Ads and Marketing Analytics
Nice to havePrevious work in marketing technology, advertising platforms, growth optimization, or customer acquisition analytics providing domain expertise.
Distributed Computing
Nice to haveFamiliarity with Apache Spark, distributed data processing systems, or cloud computing platforms for handling large-scale ML workloads.
MLOps and Model Deployment
Nice to haveExperience with model monitoring, automated retraining, feature stores, model serving infrastructure, and DevOps practices for ML systems.
Bayesian Methods
Nice to haveKnowledge of Bayesian statistics, probabilistic programming, or Bayesian optimization for decision-making under uncertainty.
Tech stack
Languages
Frameworks
Databases
Tools
Other
Compensation
Pay and benefits.
Base·USD 381,000 – 423,000
Full posting
Original listing.
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.
Our team partners closely with Product, Engineering, Data Science, Design, Finance, Lifecycle, and Growth Marketing. We move quickly, test rigorously, and hold a high bar for measurement, privacy, reliability, and user trust.
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.
The challenge is to make reliable decisions from noisy, delayed, and incomplete signals. You will combine machine learning, optimization, and experimentation to improve incremental growth and long-term user value within budget and efficiency constraints. You will own this work from problem formulation and prototyping through deployment, evaluation, and ongoing operation.
This role is based in San Francisco, CA. We use a hybrid work model of three days in the office per week and offer relocation assistance to eligible employees.
In this role, you will:
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. Build in spend limits, monitoring, and safe fallback behavior.
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. Partner with Data Science to measure incrementality and validate whether offline improvements translate into better investment outcomes.
Own reliable production systems. Build data pipelines, training and inference workflows, and integrations that turn model outputs into budget decisions. Establish data-quality checks, monitoring, and retraining with thoughtful treatment of privacy and consent.
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.
You might thrive in this role if you:
5+ years of experience as a Machine Learning Engineer, Applied Scientist, Data Scientist, or in a comparable quantitative engineering role, or an equivalent record of impact.
Experience building and operating production ML or quantitative decision systems, from problem formulation and data pipelines through deployment, experimentation, and monitoring.
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, and the judgment to balance model quality, causal impact, and operational reliability.
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, in service of OpenAI’s mission to ensure that general-purpose artificial intelligence benefits all of humanity.
About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
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