Engineering Manager, MLE
OpenAI6 days ago
Location
San Francisco
Type
Full Time
Salary
USD 293,000 – 385,000
Level
Manager
Role
Engineering Manager, ML
Posted
Jun 23, 2026
Full TimeManager
The role
Summary
Lead OpenAI's Integrity team as an Engineering Manager specializing in machine learning, overseeing the design and deployment of advanced ML models that detect content abuse and prevent scaled attacks on our platforms. This role combines hands-on machine learning expertise with people leadership, requiring deep knowledge of LLM training, transformer architectures, and large-scale ML system deployment to safeguard user experience and platform stability. You'll mentor engineering talent, collaborate with researchers and product teams, and drive technical strategy for mission-critical safety and trust systems.
What you'll do
Lead Machine Learning Architecture & Strategy: Define and drive the technical strategy for the Integrity team's machine learning systems, overseeing the design of advanced classifiers and content understanding models that detect abuse, scaled attacks, and misuse patterns. Make architectural decisions that balance innovation with production reliability, ensuring systems scale safely and effectively as OpenAI's platforms grow.
Deploy Advanced ML Models to Production: Lead the design and deployment of state-of-the-art machine learning models from research concept to production implementation. Ensure models meet production requirements for latency, throughput, and accuracy while maintaining robustness against adversarial threats and edge cases that could undermine platform integrity.
Mentor & Develop Engineering Talent: Build and mentor a high-performing machine learning engineering team. Conduct technical interviews, provide career development guidance, lead code reviews to maintain engineering standards, and foster knowledge-sharing practices. Develop team members' expertise in LLM training, model optimization, and production ML systems.
Optimize & Scale Data Pipelines: Oversee implementation of scalable data infrastructure and pipelines that support model training and inference at scale. Optimize models for both performance (latency, throughput) and accuracy, ensuring efficient resource utilization and cost-effectiveness. Drive continuous improvement in data quality and pipeline reliability.
Drive Research-to-Production Excellence: Bridge the gap between cutting-edge machine learning research and production systems. Collaborate closely with research teams to implement research breakthroughs, evaluate novel architectures and approaches, and ensure research innovations translate into tangible improvements in platform trust and safety.
Establish & Maintain Production Excellence: Monitor and maintain deployed models to ensure continuous performance, reliability, and safety. Establish monitoring practices, alerting systems, and incident response procedures. Drive a culture of operational excellence that balances innovation velocity with system stability and safety.
Cross-Functional Collaboration & Communication: Work closely with product managers, software engineers, security teams, and researchers to understand complex business challenges related to content moderation, user safety, and platform integrity. Translate business requirements into technical specifications and ensure alignment on priorities amid potentially competing demands.
Set Technical Standards & Best Practices: Establish coding standards, architectural patterns, and engineering practices that promote high-quality code, reliability, and maintainability across the team. Lead by example in demonstrating technical excellence and commitment to sustainable, scalable engineering practices.
What we look for
Technical
Machine Learning & Deep LearningAdvanced proficiency in machine learning methodologies, neural network design, and deep learning frameworks. Strong understanding of transformer architectures, attention mechanisms, and state-of-the-art model training techniques applicable to large-scale language models.
Python ProgrammingExpert-level Python programming skills for ML development, data processing, and system implementation. Ability to write production-quality code with attention to performance optimization and maintainability.
LLM Training & Fine-TuningHands-on expertise with training methodologies including supervised fine-tuning, reinforcement learning from human feedback, policy optimization algorithms, and knowledge distillation. Experience optimizing large language models for specific downstream tasks and safety objectives.
Distributed Systems & ScalabilityUnderstanding of distributed computing, data pipelines, and scalable architecture patterns. Experience optimizing machine learning systems to handle production-scale data volumes and inference latency requirements.
Team Leadership & Technical StrategyAbility to define technical direction, make architectural decisions, conduct effective code reviews, and establish engineering practices that maintain code quality and system reliability at scale.
Education
Master's Degree in Computer Science, Machine Learning, or Related FieldAdvanced degree providing strong theoretical foundation in computer science fundamentals, machine learning theory, algorithms, and statistical methods. Equivalent demonstrated expertise through professional experience may be considered.
PhD in Computer Science, Machine Learning, Data Science, or Related Discipline (Preferred)Doctoral degree providing deep theoretical knowledge and research experience in machine learning, artificial intelligence, or related fields. Demonstrates expertise in pushing forward the state-of-the-art in AI research and development.
Experience
Production Machine Learning SystemsSignificant experience designing, developing, and maintaining machine learning models in production environments. Track record of shipping models that deliver measurable business impact and scale reliably under production demands.
Team Leadership & MentorshipDemonstrated experience leading technical teams, mentoring engineers, establishing best practices, and driving technical excellence. Proven ability to grow team capabilities and foster a culture of continuous learning.
Large Language Model WorkHands-on professional experience working with large language models, including training, fine-tuning, evaluation, and deployment. Understanding of LLM capabilities, limitations, and optimization strategies for production use.
Cross-Functional CollaborationExperience working effectively with product teams, researchers, and stakeholders to translate business objectives into technical solutions. Demonstrated ability to communicate complex technical concepts to non-technical audiences.
Skills
Required skills
Machine Learning Model DevelopmentExpertise in designing, training, and deploying production-grade machine learning models, including deep learning architectures and large language models (LLMs). Demonstrated ability to translate research concepts into scalable, production-ready solutions with measurable impact on platform safety and user experience.
Large Language Model ExpertiseAdvanced proficiency with transformer-based models and techniques for training and fine-tuning LLMs, including supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), policy optimization, and knowledge distillation. Experience optimizing models for performance, safety, and inference efficiency at scale.
Deep Learning FrameworksExpert-level proficiency with PyTorch or TensorFlow for implementing and optimizing neural network architectures. Strong understanding of GPU optimization, distributed training, and model deployment pipelines essential for large-scale ML systems.
Software Engineering FundamentalsStrong foundation in computer science principles including data structures, algorithms, software architecture patterns, and best practices in code organization. Ability to write clean, maintainable, and efficient code that meets production standards.
Engineering Leadership & Team ManagementProven experience leading and mentoring engineering teams, conducting code reviews, fostering knowledge sharing, establishing engineering best practices, and maintaining high technical standards. Demonstrated ability to scale team capabilities and develop talent.
Cross-Functional CollaborationExperience working effectively with researchers, software engineers, product managers, and stakeholders to translate business requirements into technical solutions. Strong communication skills for complex technical concepts to diverse audiences.
Problem-Solving Under AmbiguityProactive approach to problem-solving with excellent analytical skills. Ability to thrive in fast-paced, loosely-defined environments with competing priorities, taking ownership of end-to-end problems and acquiring knowledge as needed to drive solutions forward.
Nice to have
Content Understanding & Abuse PreventionDirect experience building machine learning systems for content moderation, abuse detection, adversarial threat detection, or trust and safety applications. Familiarity with classification approaches for detecting harmful content, policy violations, or scaled attack vectors.
MLOps & Production ML SystemsExperience deploying, monitoring, and maintaining machine learning models in production environments. Knowledge of data pipelines, model versioning, A/B testing frameworks, and continuous monitoring for model performance degradation and safety issues.
Research-to-Production ExcellenceTrack record of converting cutting-edge machine learning research into reliable, scalable production systems. Understanding of how to bridge the gap between academic research and enterprise-grade deployment with high reliability requirements.
Adversarial Machine LearningKnowledge of adversarial robustness, threat modeling for ML systems, and techniques for building classifiers resilient to malicious inputs or adversarial attacks. Understanding of security considerations specific to AI systems.
Compensation & benefits
Salary
USD 293,000 – 385,000 (annual)
Stock options
Available
Benefits
Competitive Equity & Stock Options
Participate in OpenAI's growth through meaningful equity grants, aligning your success with company success as we advance artificial intelligence technology.
Comprehensive Health Coverage
Premium medical, dental, and vision insurance plans covering you and your family, ensuring comprehensive healthcare protection.
Retirement & Financial Planning
401(k) retirement plan with company matching to support long-term financial security and retirement planning.
Generous Time Off
Flexible paid time off (PTO) policy and generous vacation allowance, enabling work-life balance and recovery time.
Professional Development & Learning
Investment in continuous learning through conference attendance, training programs, and access to cutting-edge resources to stay current with AI/ML advancements.
Mentorship & Career Growth
Work with world-class AI researchers and engineers, providing exceptional opportunities for skill development and career advancement in artificial intelligence.
Mission-Driven Work
Contribute directly to OpenAI's mission of ensuring artificial intelligence benefits all of humanity, working on problems with significant societal impact.
Collaborative Culture
Join a dynamic, innovative team where ideas flow freely, creativity is encouraged, and collaboration across disciplines thrives.
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OpenAI is an American artificial intelligence research organization developing advanced AI models like GPT. Focused on ensuring AI benefits humanity, it creates tools for natural language processing and generative AI applications.
San Francisco, California, United StatesFounded 2015openai.com
Tech Stack
Languages
PythonSQL
Frameworks
PyTorchTensorFlowTransformers (Hugging Face)
Databases
Data Warehousing SolutionsVector Databases
Tools
Git & Version ControlMLOps & Monitoring ToolsJupyter & Development EnvironmentsCloud Platforms
Other
Large Language Models (LLMs)Adversarial Robustness & SecurityContent Moderation & Abuse PreventionDistributed Training & GPU Optimization
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