Scale Engineer

Machine Learning Engineer, Monetization AI/ML

ML Engineer · Mid · Full Time

San FranciscoUSD 381k – 555k1mo ago
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Role

What you'll do.

As a Machine Learning Engineer in OpenAI's Monetization AI/ML group, you'll design and deploy state-of-the-art large language models and machine learning systems that power next-generation monetization products at global scale. This greenfield role combines cutting-edge AI research with production engineering, requiring expertise in deep learning, transformers, and large language model optimization to build privacy-preserving systems that scale AI access responsibly.

Responsibilities

  • Design and Deploy Advanced ML Models: Architect and implement state-of-the-art machine learning models including transformer-based architectures and large language models for real-world production environments. Transform research breakthroughs into deployable AI systems that directly impact OpenAI's monetization products and user experiences across global platforms.
  • Optimize Models for Production Scale: Develop scalable data pipelines and implement performance optimization techniques including model distillation, quantization, and inference acceleration. Ensure machine learning models meet production-readiness standards for latency, accuracy, and resource efficiency in high-traffic environments.
  • Collaborate on Cross-Functional AI Solutions: Partner with researchers, software engineers, product managers, and design teams to translate complex business challenges into AI-powered solutions. Work in agile cycles through prototyping, experimentation, and iterative deployment to validate hypotheses and refine model performance.
  • Train and Fine-tune Large Language Models: Apply advanced techniques including supervised fine-tuning, reinforcement learning from human feedback (RLHF), policy optimization, and knowledge distillation to adapt foundation models for specific monetization use cases while maintaining safety and privacy standards.
  • Monitor and Maintain Deployed Systems: Implement comprehensive monitoring, observability, and alerting for deployed machine learning models in production. Conduct ongoing analysis of model performance, data drift, and business metrics to ensure systems continue delivering measurable value and maintain alignment with OpenAI's safety standards.
  • Maintain Engineering Excellence: Lead by example through rigorous code reviews, knowledge sharing, and adherence to software engineering best practices. Stay current with advances in machine learning, transformers research, and LLM techniques while fostering a culture of technical rigor and continuous learning.

Qualifications

What we look for.

Technical

  • Deep Learning and Transformer Models

    Demonstrated expertise in designing and implementing deep neural networks with particular proficiency in transformer architectures, attention mechanisms, and modern large language model principles. Must understand model internals, training dynamics, and optimization strategies for attention-based systems.

  • Large Language Model Optimization

    Hands-on experience with training, fine-tuning, and deploying large language models using techniques such as supervised fine-tuning, reinforcement learning from human feedback, policy optimization, and knowledge distillation. Familiarity with prompt engineering and model adaptation strategies.

  • Deep Learning Frameworks

    Expert-level proficiency in PyTorch or TensorFlow with ability to build custom training loops, implement distributed training strategies, and optimize model performance. Strong understanding of framework capabilities for research implementation and production deployment.

  • Software Engineering Fundamentals

    Strong foundation in data structures, algorithms, computational complexity analysis, and software design principles. Ability to write clean, maintainable, well-tested code and implement solutions that scale efficiently.

  • Machine Learning Systems Design

    Experience designing end-to-end machine learning pipelines including data preprocessing, feature engineering, model training, evaluation, and deployment. Understanding of ML systems architecture, experiment tracking, and reproducibility best practices.

  • Production ML Systems

    Experience deploying and maintaining machine learning models in production environments. Understanding of model serving, containerization, monitoring, and the engineering practices required to keep ML systems reliable at scale.

Education

  • Master's or PhD in Machine Learning

    Advanced degree in Computer Science, Machine Learning, Data Science, Artificial Intelligence, or closely related field. Graduate-level education demonstrates deep understanding of ML theory, research methodologies, and ability to work at the frontier of AI innovation.

  • Strong Mathematical Foundation

    Solid grasp of linear algebra, calculus, probability theory, and statistics. Mathematical maturity required to understand model architectures, loss functions, optimization algorithms, and research literature in machine learning.

Experience

  • Deep Learning Implementation Experience

    3+ years of professional or research experience implementing and deploying deep learning models, with specific focus on transformer-based architectures, attention mechanisms, or large language models in research or production environments.

  • Advanced ML Techniques

    Proven track record developing and optimizing machine learning systems using techniques like supervised fine-tuning, reinforcement learning, policy optimization, or knowledge distillation. Experience working with cutting-edge ML research applied to practical problems.

  • Cross-Functional Collaboration

    Experience working effectively with researchers, engineers, product managers, and designers in fast-paced environments. Demonstrated ability to translate business requirements into technical specifications and communicate complex ML concepts across disciplines.

Skills

Required

  • PyTorch

    Expert proficiency in PyTorch framework including custom modules, autograd, distributed training, and optimization techniques for research and production machine learning systems.

  • Deep Learning Architectures

    In-depth knowledge of transformer models, attention mechanisms, and modern neural network designs. Ability to implement, modify, and optimize these architectures from scratch.

  • Large Language Models

    Strong hands-on experience with LLM training, fine-tuning, inference optimization, and evaluation. Understanding of prompt engineering, in-context learning, and model adaptation strategies.

  • Python Programming

    Expert-level Python development skills with ability to write production-quality code, debug complex systems, and optimize performance-critical implementations.

  • Machine Learning Mathematics

    Strong foundation in linear algebra, calculus, probability, and statistics as applied to machine learning. Ability to understand and implement optimization algorithms and statistical methods.

  • Data Processing and Pipelines

    Experience building scalable data preprocessing pipelines, feature engineering, and handling large datasets efficiently. Understanding of distributed data processing and ETL concepts.

Preferred

  • Search Relevance and Ranking Systems

    Nice to have

    Prior experience building or optimizing learning-to-rank systems, ranking models, or search relevance algorithms. Knowledge of A/B testing methodologies for ranking optimization.

  • Ads Technology and Systems

    Nice to have

    Experience working on advertising systems, click-through rate prediction, bid optimization, or monetization algorithms. Understanding of ads ecosystem, auction mechanisms, and relevant ML applications.

  • Reinforcement Learning

    Nice to have

    Practical experience implementing reinforcement learning algorithms, policy optimization, or RLHF techniques. Understanding of reward modeling and alignment techniques relevant to LLM systems.

  • TensorFlow

    Nice to have

    Additional proficiency in TensorFlow framework, Keras, or other deep learning frameworks. Multi-framework experience demonstrates flexibility and depth of ML engineering knowledge.

  • Distributed Systems

    Nice to have

    Experience with distributed machine learning training, parallelization strategies, and large-scale data processing systems. Knowledge of GPU/TPU utilization and multi-machine training.

  • Model Deployment and Serving

    Nice to have

    Experience deploying ML models to production using containerization, Kubernetes, model serving frameworks, or cloud platforms. Understanding of model versioning and A/B testing infrastructure.

Tech stack

Languages

PythonCUDA/C++

Frameworks

PyTorchTensorFlowHugging Face TransformersJAXRay

Databases

PostgreSQLRedisBigQuery or Snowflake

Tools

Weights and Biases (W&B)Git/GitHubDocker and KubernetesJupyter NotebooksDatadog or New Relic

Other

Cloud Platforms (AWS, GCP, Azure)MLOps Best PracticesPrivacy and Safety in AI

Compensation

Pay and benefits.

Base·USD 381,000 – 555,000

Equity·Stock options

Benefits

  • Competitive Equity Compensation

    Significant stock options and equity participation reflecting OpenAI's position as a leading AI research and deployment company. Equity represents meaningful ownership in a transformative technology organization.

  • Comprehensive Health Insurance

    Medical, dental, and vision coverage with employer contributions for employees and eligible dependents. Coverage designed to support employee wellness across preventive, treatment, and mental health services.

  • 401(k) Retirement Plan

    Employer-sponsored retirement savings plan with matching contributions. Support for long-term financial planning and retirement security for eligible employees.

  • Unlimited Paid Time Off

    Flexible paid time off policy allowing employees to take time off as needed for vacation, personal development, and wellness. Trust-based approach to work-life balance and personal well-being.

  • Professional Development and Learning

    Opportunities to stay at the forefront of AI research through conferences, training, research collaborations, and access to OpenAI's research teams. Budget for continuous learning and technical growth in rapidly evolving field.

  • Collaborative Research Environment

    Work alongside world-leading AI researchers and engineers. Access to cutting-edge infrastructure, compute resources, and internal research breakthroughs powering the most advanced AI systems.

  • Flexible Work Arrangements

    Flexibility in how and where you work, balancing deep focus time with collaborative team interactions. Support for working arrangements that enable your best productivity and well-being.

  • Wellness and Mental Health Support

    Mental health resources, counseling services, and wellness programs supporting employee well-being. Recognition that working on frontier AI challenges requires comprehensive support systems.

  • Parental Leave and Family Support

    Comprehensive parental leave policies and family support benefits. Programs designed to support employees at different life stages.

  • Mission-Driven Culture

    Opportunity to work on problems with global significance—developing AI that benefits all of humanity. Deep sense of purpose through contributing to the safe development and responsible deployment of transformative technology.

Process

Interview steps.

  1. 01

    Initial Screening Call

    Recruiter conversation to discuss background, motivations for the role, and alignment with OpenAI's mission. Initial assessment of experience with machine learning, deep learning frameworks, and career trajectory. Typically 30-45 minutes.

  2. 02

    Technical Phone Screen

    First technical evaluation with an OpenAI engineer covering machine learning fundamentals, deep learning concepts, Python programming, and problem-solving approach. Discussion of past projects involving transformers, LLMs, or production ML systems. Typically 60 minutes.

  3. 03

    Coding and ML Design Challenge

    Take-home or virtual technical assessment focusing on machine learning system design, PyTorch/TensorFlow implementation, or algorithm design problems relevant to monetization and ranking systems. Demonstrates practical ML engineering skills and code quality. Usually 2-4 hours to complete.

  4. 04

    Research-Focused Technical Interview

    Deep dive with senior researcher or engineer on advanced machine learning concepts including transformer architectures, training optimization, fine-tuning techniques, and LLM systems. Discussion of research papers, model optimization strategies, and creative problem-solving approaches. Typically 60-75 minutes.

  5. 05

    System Design Interview

    Evaluation of ability to design scalable machine learning systems end-to-end, from data pipelines through model training to production deployment. Discussion of architecture decisions, tradeoffs, monitoring, and how to handle production challenges at OpenAI's scale.

  6. 06

    Cross-Functional Collaboration Round

    Conversation with product manager, researcher, or designer to assess collaboration skills, communication ability, and understanding of how to translate business needs into technical solutions. Evaluation of ability to work effectively in fast-paced, loosely-defined environments.

  7. 07

    Hiring Committee Review

    Senior leadership review of all interview feedback to assess technical depth, research capability, collaboration fit, and alignment with OpenAI's values of safety and responsible AI development. Final decision made at this stage.

  8. 08

    Offer and Background Check

    Offer extended pending successful background check administered in accordance with applicable law. For US-based candidates, includes consideration under fair chance ordinances relevant to applicant location.

Full posting

Original listing.

About the Team

The Monetization team is a new cross-functional group working across engineering, product, research, and design to build the foundational systems that will help OpenAI scale access to intelligence responsibly. Our mission is to develop user-first, privacy-preserving monetization products—including next-generation ads experiences—that strengthen user trust, unlock economic opportunity, and support OpenAI’s long-term innovation.

Monetization plays a critical role in enabling OpenAI to continue pushing the boundaries of AI capabilities while ensuring the benefits of AGI are broadly shared. We believe monetization must be aligned with user value, uphold rigorous privacy and safety standards, and sustain a healthy ecosystem of developers and businesses.

This team operates in a greenfield environment and moves quickly through prototyping, experimentation, and iterative deployment. We partner closely with Product, Design, and Research to bring research breakthroughs into real-world systems at global scale.

About the Role

As a Research Engineer in OpenAI's Monetization Group, you will have the opportunity to work with some of the brightest minds in AI. You'll contribute to deploying state-of-the-art models in production environments, helping turn research breakthroughs into tangible solutions. If you're excited about making AI technology accessible and impactful, this role is your chance to make a significant mark.

In this role, you will:

  • Innovate and Deploy: Design and deploy advanced machine learning models that solve real-world problems. Bring OpenAI's research from concept to implementation, creating AI-driven applications with a direct impact.

  • Collaborate with the Best: Work closely with researchers, software engineers, and product managers to understand complex business challenges and deliver AI-powered solutions. Be part of a dynamic team where ideas flow freely and creativity thrives.

  • Optimize and Scale: Implement scalable data pipelines, optimize models for performance and accuracy, and ensure they are production-ready. Contribute to projects that require cutting-edge technology and innovative approaches.

  • Learn and Lead: Stay ahead of the curve by engaging with the latest developments in machine learning and AI. Take part in code reviews, share knowledge, and lead by example to maintain high-quality engineering practices.

  • Make a Difference: Monitor and maintain deployed models to ensure they continue delivering value. Your work will directly influence how AI benefits individuals, businesses, and society at large.

You might thrive in this role if you:

  • Master's/ PhD degree in Computer Science, Machine Learning, Data Science, or a related field.

  • Demonstrated experience in deep learning and transformers models

  • Proficiency in frameworks like PyTorch or Tensorflow

  • Strong foundation in data structures, algorithms, and software engineering principles.

  • Experience with search relevance, ads ranking or LLMs is a plus.

  • Are familiar with methods of training and fine-tuning large language models, such as distillation, supervised fine-tuning, and policy optimization

  • Excellent problem-solving and analytical skills, with a proactive approach to challenges.

  • Ability to work collaboratively with cross-functional teams.

  • Ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines

  • Enjoy owning the problems end-to-end, and are willing to pick up whatever knowledge you're missing to get the job done

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.

To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form. No response will be provided to inquiries unrelated to job posting compliance.

We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.

OpenAI Global Applicant Privacy Policy

At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

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