Machine Learning Engineer, API Multicloud
ML Engineer · Mid · Full Time
Opens OpenAI's application page
Role
What you'll do.
Join OpenAI's API Multicloud team to build and improve production ML systems that help strategic partners adapt advanced AI models to critical use cases in cloud-native environments. This Machine Learning Engineer role spans post-training workflows, evaluation, data pipelines, model behavior, and infrastructure integration, requiring strong deep learning expertise, large language model fine-tuning experience, and the ability to operate across ambiguous 0→1 problems at the intersection of AI systems, developer platforms, and distributed infrastructure.
Responsibilities
- Partner with Strategic Customers and Internal Teams: Collaborate with external technical partners and cross-functional teams to define target model behaviors, diagnose failure modes, and translate real-world requirements into actionable training, evaluation, and system specifications that drive measurable product improvements.
- Build and Scale Production ML Systems: Design, develop, and operationalize production-grade machine learning systems for model customization, post-training, and fine-tuning-as-a-service workflows that enable AWS-hosted capabilities and support enterprise-scale deployments across strategic cloud environments.
- Design and Execute ML Experiments: Architect, conduct, and interpret comprehensive experiments to validate whether training and customization workflows achieve intended outcomes, utilizing rigorous evaluation methodologies to identify and implement improvements across data, training procedures, evaluation frameworks, and infrastructure components.
- Integrate ML Capabilities into Cloud Infrastructure: Partner with backend and infrastructure engineers to seamlessly integrate machine learning capabilities into AWS-native API environments, ensuring production reliability, scalability, and performance across distributed systems.
- Drive Platform Improvements: Synthesize learnings from partner deployments and real-world use cases to propose, implement, and iterate on improvements to post-training systems, developer APIs, tooling, and workflows that enhance platform capabilities and developer experience.
- Collaborate with Research and Applied Teams: Work closely with Research and Applied teams to productionize model improvements, training workflows, and evaluation best practices, translating cutting-edge research into reliable, scalable systems that deliver consistent results.
- Design Safe Model Customization Systems: Architect systems and safeguards that enable strategic partners and enterprise customers to safely customize and fine-tune OpenAI models for high-value use cases while maintaining safety standards and mitigating potential risks.
- Debug Complex Distributed Systems: Diagnose and resolve issues spanning model behavior, training data quality, API performance, distributed infrastructure, and customer-facing product surfaces, leveraging deep systems thinking and cross-domain expertise.
- Operate with High Ownership in Early-Stage Environments: Take end-to-end ownership of ambiguous 0→1 problems in rapidly evolving systems, demonstrating comfort with ambiguity, autonomous problem-solving, and the ability to learn and adapt as requirements and systems evolve.
Qualifications
What we look for.
Technical
Deep Learning and Transformer Models
Hands-on expertise with deep learning frameworks (PyTorch or TensorFlow), transformer architecture, and building neural network systems from design through production deployment.
Large Language Model Fine-Tuning
Proven experience fine-tuning and customizing large language models using techniques including supervised fine-tuning, knowledge distillation, preference optimization (RLHF), reinforcement learning, and other post-training methodologies.
Production ML Systems
Experience building, training, evaluating, and deploying production machine learning systems with attention to reliability, scalability, monitoring, and performance optimization.
Data Pipelines and Evaluation Frameworks
Expertise designing and implementing robust data pipelines, evaluation systems, and metrics that enable rigorous model assessment and provide actionable insights for continuous improvement.
Distributed Systems and Cloud Infrastructure
Strong understanding of distributed computing concepts, cloud infrastructure architecture (particularly AWS), and the tradeoffs inherent in production ML platform design.
High-Quality Production Code
Proficiency writing clean, well-tested, maintainable code in Python, Rust, or similar languages with strong software engineering fundamentals including data structures, algorithms, and systems design principles.
Education
Advanced Degree in Computer Science or Machine Learning
Master's or PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent demonstrated professional ML engineering experience.
Experience
3+ Years ML and Infrastructure Engineering
Minimum 3 years of professional engineering experience in machine learning, infrastructure, or product-driven engineering roles, demonstrating progression and impact on production systems.
Model Behavior and Customization
Demonstrated experience understanding, analyzing, and improving model behavior through customization, fine-tuning, and post-training techniques in production environments.
Cross-Functional Collaboration
Proven ability to operate effectively across diverse technical domains including model behavior, APIs, infrastructure, and business requirements while collaborating with Research, Safety, product, and external partners.
Autonomous Problem-Solving
Track record of operating independently through ambiguous requirements, owning problems end-to-end, and rapidly acquiring domain knowledge needed to execute at high standards.
Skills
Required
PyTorch or TensorFlow
Production-level expertise with modern deep learning frameworks for model development, training, and deployment.
Python
Advanced proficiency in Python for ML systems, data pipelines, and production code with strong software engineering practices.
Machine Learning System Design
Ability to design end-to-end ML systems including data ingestion, training, evaluation, serving, and monitoring.
Model Fine-Tuning and Post-Training
Hands-on experience with supervised fine-tuning, RLHF, distillation, preference learning, and other advanced training techniques.
Experimental Design and Evaluation
Rigorous approach to designing experiments, selecting appropriate metrics, and interpreting results to drive product decisions.
Distributed Computing
Understanding of distributed training, data parallelism, and orchestration of large-scale ML workloads.
Preferred
Rust
Nice to haveExperience with Rust for systems programming, performance-critical infrastructure, or production ML systems.
AWS Services
Nice to haveFamiliarity with AWS services including EC2, SageMaker, Lambda, and other cloud infrastructure relevant to ML deployment.
LLM Safety and Alignment
Nice to haveBackground in AI safety, model alignment, or techniques for ensuring large language models behave safely and predictably.
Customer-Facing API Development
Nice to haveExperience designing and iterating on APIs serving external customers or partners with complex technical requirements.
ML Tooling and Infrastructure
Nice to haveExperience building or working extensively with ML platforms, workflow orchestration tools, or infrastructure supporting model development.
Open Source Contribution
Nice to haveActive contributions to ML-related open source projects or demonstrated expertise with modern ML frameworks and libraries.
Tech stack
Languages
Frameworks
Databases
Tools
Other
Compensation
Pay and benefits.
Base·USD 295,000 – 445,000
Equity·Stock options
Benefits
Equity and Stock Options
Significant equity stake in OpenAI, one of the most prominent AI research organizations, providing substantial upside potential as the company continues to advance AI capabilities and product commercialization.
Comprehensive Health Insurance
Competitive medical, dental, and vision coverage for employees and their families, reflecting commitment to employee wellness.
Retirement Planning
401(k) retirement savings plan with employer matching to support long-term financial security.
Flexible Work Environment
Opportunity to work on cutting-edge AI problems in a collaborative, fast-paced environment with access to world-class research and infrastructure.
Professional Development
Continuous learning opportunities through collaboration with leading AI researchers, access to training resources, and exposure to state-of-the-art ML techniques.
Competitive Time Off
Generous vacation policy and flexibility to maintain work-life balance while executing on high-impact projects.
Mission-Driven Culture
Opportunity to contribute to AI safety, responsible deployment, and ensuring that advanced AI benefits all of humanity.
Process
Interview steps.
- 01
Initial Application and Resume Review
OpenAI will review your application, resume, and background to assess alignment with core technical requirements, experience in production ML systems, and evidence of working with large language models or similar advanced AI systems.
- 02
Screening Call with Recruiter
Preliminary conversation to discuss your background, motivation for joining OpenAI, understanding of the API Multicloud team's mission, and high-level technical capabilities. Expect questions about your most impactful ML projects and experience with cross-functional collaboration.
- 03
Technical Depth Interview
Deep-dive technical conversation with current ML engineers covering your hands-on experience with deep learning frameworks, model fine-tuning techniques, production ML system design, and specific examples of how you've solved ambiguous technical problems in previous roles.
- 04
System Design and Problem-Solving
Discussion of how you would approach designing ML systems for model customization, post-training workflows, or evaluation frameworks. Expect scenarios involving trade-offs between accuracy, latency, scalability, and safety considerations in production environments.
- 05
Cross-Functional Collaboration Assessment
Conversation exploring your experience working with diverse teams (Research, Safety, infrastructure), managing ambiguous requirements from external partners, and examples of how you've translated complex stakeholder needs into technical solutions.
- 06
Leadership and Ownership Discussion
Discussion of your approach to operating independently in early-stage (0→1) environments, how you set priorities amid ambiguity, and specific instances where you took ownership of end-to-end problems with unclear paths forward.
- 07
Final Round with Leadership
Conversation with team leadership or senior management covering your long-term technical interests, vision for advancing ML systems, alignment with OpenAI's mission around safe AI deployment, and questions about the role and company.
Full posting
Original listing.
About the Team
OpenAI’s API Multicloud team is responsible for extending OpenAI’s API platform into strategic cloud environments, starting with AWS. The team’s mission is to distribute OpenAI’s API broadly and safely by enabling key API technologies in AWS-native environments, in close partnership with Amazon and internal teams across Codex, Research, Safety Systems, and Applied.
The team is focused on bringing core developer and enterprise capabilities into cloud-native environments, including AWS-hosted Codex, model customization / post-training as a service, and new stateful runtime environments for agentic workloads. This work sits at the intersection of production ML systems, developer platforms, model behavior, and large-scale infrastructure.
About the Role
We’re hiring Machine Learning Engineers to build and improve the AI systems that help strategic partners adapt OpenAI models to important use cases in cloud-native environments. This role spans post-training workflows, evaluation, data pipelines, model behavior, and API/infrastructure integration.
You’ll work at the boundary between partner needs and core ML systems: helping teams understand what is and isn’t working, diagnosing issues in training and evaluation workflows, and turning those learnings into improvements to the underlying platform. You should enjoy working with external technical partners, extracting the real goal from messy requests, and pushing back or reframing when the requested experiment is not the highest-leverage path. You’ll collaborate closely with Research, Applied, Safety Systems, infrastructure teams, and external technical partners to solve ambiguous model-performance problems. When you succeed, strategic partners and internal teams will be able to improve model behavior with confidence, driving measurable product improvements while the systems behind that work become more reliable, scalable, and effective over time.
In this role, you will
Partner with strategic customers and internal teams to define target model behaviors, diagnose failure modes, and translate real-world needs into training, evaluation, and system requirements.
Build and scale production ML systems for model customization, post-training, and fine-tuning-as-a-service workflows.
Design, run, and interpret experiments to see whether training and customization workflows are producing the intended outcomes, and identify changes to data, evaluation, training, or infrastructure that improve performance.
Partner with backend and infrastructure engineers to integrate ML capabilities into AWS-native API environments.
Feed learnings from partner deployments back into the platform by proposing and implementing improvements to post-training systems, tooling, APIs, and developer workflows.
Work closely with Research and Applied teams to bring model improvements, training workflows, and evaluation best practices into production.
Help design systems that allow strategic partners and enterprise customers to safely customize OpenAI models for high-value use cases.
Debug and improve complex systems spanning model behavior, training data, APIs, distributed infrastructure, and customer-facing product surfaces.
Operate with high ownership in a 0→1 environment where requirements are ambiguous, systems are evolving quickly, and reliability matters.
Your background might look something like:
Master’s or PhD in Computer Science, Machine Learning, or a related field, or equivalent practical experience.
3+ years of professional engineering experience in relevant ML, infrastructure, or product-driven engineering roles.
Strong ML engineering experience building, training, fine-tuning, evaluating, or deploying production AI systems, with hands-on experience in deep learning, transformer models, and frameworks like PyTorch or TensorFlow.
Hands-on experience with training and fine-tuning large language models, including methods like supervised fine-tuning, distillation, preference optimization, reinforcement learning, or other post-training techniques.
Strong software engineering fundamentals, including data structures, algorithms, systems design, and high-quality production code in Python, Rust, or similar languages.
Experience with model customization, evaluation systems, data pipelines, distributed systems, cloud infrastructure, or production ML platform tradeoffs.
Ability to operate across model behavior, APIs, and infrastructure, while collaborating closely with Research, Safety, product engineering, infrastructure, and external technical partners.
Comfort moving quickly through ambiguity, owning problems end-to-end, and learning whatever is needed 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.
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We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.
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