OpenAI

Engineering Manager, Model Flywheel

OpenAI5 days ago
Location

San Francisco

Type

Full Time

Salary

USD 293,000 – 385,000

Level

Manager

Role

Engineering Manager

Posted

Jul 14, 2026

Full TimeManager

The role

Summary

Lead engineering excellence for OpenAI's ChatGPT Model Capabilities and Deployment team as an Engineering Manager, overseeing model experimentation, safe deployment automation, and comprehensive measurement systems. This role demands proven experience building and scaling production systems at enterprise scale, with deep expertise in large language model deployment lifecycle, distributed infrastructure, and cross-functional stakeholder management. You'll drive critical technical initiatives that directly impact millions of users while shaping the future of AI safety, reliability, and user experience.

What you'll do

Lead Model Experimentation Infrastructure: Design, develop, and optimize rapid experimentation frameworks for ChatGPT and Codex product validation. Establish and maintain automated lifecycle management systems that enable safe model testing and evaluation across multiple deployment tiers. Drive innovation in experiment design patterns and automate complex testing workflows to accelerate model iteration cycles.
Oversee Safe Model Deployment and Rollout Strategy: Architect and lead robust model deployment systems ensuring safe, scalable rollout of new capabilities. Develop sophisticated rollout automation including canary deployments, staged rollouts, and automated rollback mechanisms. Implement operational tooling that provides comprehensive visibility into model performance and system health during and after deployment.
Manage Capacity Planning and Infrastructure Operations: Build automated capacity management systems that optimize resource utilization across ChatGPT's serving infrastructure. Integrate platform-wide health monitoring and predictive scaling mechanisms. Establish operational runbooks and incident response protocols that maintain system reliability at scale while supporting continuous model innovation.
Build Comprehensive Model Measurement and Evaluation Systems: Design end-to-end measurement frameworks encompassing model quality evaluation, user signal integration, grader systems, and launch scorecards. Establish feedback loops that connect user metrics, research insights, and product goals. Create visibility dashboards and reporting systems that enable data-driven decision-making across research, product, and engineering teams.
Elevate ChatGPT's Core Systems and Frameworks: Drive modernization of ChatGPT's harness infrastructure, context management systems, and system prompt frameworks. Consolidate legacy systems and establish scalable abstractions that enable rapid feature development. Lead initiatives to improve multi-tier model experience capabilities, supporting diverse use cases and performance requirements.
Scale Self-Serve Engineering Capabilities: Expand self-serve experiment platforms enabling cross-functional teams to validate model improvements independently. Implement automated guardrails and safety checks that reduce deployment friction while maintaining rigorous quality standards. Create comprehensive documentation and training that empowers researchers and product teams.
Lead Cross-Functional Stakeholder Collaboration: Foster strong partnerships with Model Measurement Data Science, Research, Codex, Fleet, Inference, and API teams. Establish clear communication channels, dependency management, and alignment mechanisms. Navigate complex technical tradeoffs and drive consensus on architectural decisions affecting the entire ChatGPT ecosystem.
Build and Scale High-Performance Engineering Teams: Recruit, mentor, and develop world-class engineers with expertise in distributed systems, machine learning infrastructure, and large-scale systems. Establish strong engineering culture focused on quality, collaboration, and continuous learning. Drive technical career development and create pathways for team members to grow into leadership roles.

What we look for

Technical

Large-Scale Distributed Systems ArchitectureDemonstrated expertise designing and deploying large-scale distributed systems handling millions of concurrent requests. Deep understanding of service-oriented architecture, load balancing, fault tolerance, and multi-region deployment patterns. Experience optimizing latency, throughput, and reliability in complex infrastructure environments.
Machine Learning Infrastructure and DeploymentProven experience with model serving infrastructure, ML pipeline orchestration, and the complete deployment lifecycle from experimentation to production. Understanding of model versioning, A/B testing frameworks for ML, canary deployments, and shadow mode testing for AI systems.
Large Language Model Systems KnowledgeWorking knowledge of transformer-based language models, attention mechanisms, and scaling laws. Experience with LLM-specific challenges including context window management, tokenization, prompt engineering infrastructure, and multi-model systems. Understanding of safety considerations and guardrail implementations for LLM products.
Production Systems Design at ScaleTrack record shipping and maintaining production systems that serve millions of users. Deep understanding of reliability engineering, observability, monitoring, and incident response. Experience managing technical debt and scaling systems through multiple orders of magnitude.
Experimentation and Measurement Framework DesignExpertise designing and building experimentation platforms supporting A/B testing, multivariate testing, and causal inference. Experience building evaluation frameworks, metric systems, and telemetry pipelines. Understanding of statistical significance, experimental design, and avoiding pitfalls in online experimentation.
Software Engineering LeadershipDemonstrated success leading engineering teams through complex product cycles and technical challenges. Experience with technical roadmap planning, architecture decision-making, and technical debt management. Proven ability to balance engineering excellence with business velocity and user impact.

Education

Computer Science or Engineering DegreeBachelor's degree in Computer Science, Computer Engineering, or equivalent field. Advanced degree (Master's or PhD) in related field is a plus but not required with sufficient industry experience.
Continuous Learning in AI/MLCommitment to staying current with developments in machine learning, large language models, and AI infrastructure. Active engagement with research papers, open-source projects, and industry knowledge in the AI ecosystem.

Experience

Engineering Team LeadershipMinimum 5+ years leading engineering teams in complex, cross-functional environments. Success building, mentoring, and scaling teams from 3-4 engineers to 15+ members. Experience working in matrix organizations navigating multiple stakeholders and competing priorities.
Production Systems at ScaleDemonstrated success shipping and scaling production systems to hundreds of millions of users or billions of transactions. Hands-on experience with reliability engineering, capacity planning, and operational excellence in high-scale environments.
AI/ML Infrastructure or Backend ServicesStrong background in backend systems, distributed infrastructure, or machine learning platforms. Prior experience at companies with significant infrastructure challenges such as hyperscalers, infrastructure companies, or AI research organizations.
Cross-Functional CollaborationProven ability to work effectively with research teams, data scientists, product managers, and infrastructure engineers. Experience translating between technical and non-technical stakeholders and driving alignment on complex technical decisions.

Skills

Required skills

Engineering LeadershipStrategic team leadership with focus on technical direction, team development, and execution excellence. Ability to set clear technical vision while empowering engineers to make autonomous decisions.
Systems Design and ArchitectureExpert-level ability to design scalable, reliable systems handling massive scale. Experience making architectural tradeoffs and designing for operational simplicity.
Machine Learning InfrastructureDeep understanding of ML pipelines, model serving, experimentation infrastructure, and the unique challenges of deploying AI systems to production.
Distributed SystemsStrong foundation in distributed systems concepts, including consistency models, fault tolerance, replication, and coordination.
Cross-Functional CollaborationExceptional ability to work across research, product, and infrastructure teams while maintaining clear communication and alignment.
Technical CommunicationAbility to communicate complex technical concepts clearly to diverse audiences, from engineers to executives to researchers.

Nice to have

Large Language Model ExperienceHands-on experience with large language models, transformer architectures, or similar foundation models in production environments.
Experimentation PlatformsExperience building or scaling experimentation frameworks and A/B testing platforms for online products or AI systems.
ML Operations and DevOpsFamiliarity with MLOps practices, model deployment pipelines, feature stores, monitoring, and the operational side of machine learning.
Safety and Reliability EngineeringExperience implementing safety systems, guardrails, and reliability practices for mission-critical systems or AI applications.
Open Source ContributionsActive participation in open-source projects related to ML infrastructure, distributed systems, or data engineering.
Research CollaborationExperience working directly with research teams and understanding how to translate research advances into production systems.

Compensation & benefits

Salary

USD 293,000 – 385,000 (annual)

Stock options

Available

Benefits

Equity Compensation

Meaningful stock options in OpenAI, enabling you to participate in the company's growth and success as a leading AI research and deployment organization.

Comprehensive Health Insurance

Full medical, dental, and vision coverage with multiple plan options. Includes coverage for preventive care, specialist visits, and prescription medications.

Mental Health and Wellness

Access to mental health services, therapy, and wellness programs. Subsidized gym memberships, meditation apps, and wellness workshops.

Flexible Paid Time Off

Unlimited vacation policy enabling you to maintain work-life balance and recharge. Additionally, company holidays and sick leave are fully covered.

Parental Leave

Generous parental leave policies supporting both mothers and fathers, including adoption and surrogacy coverage.

Professional Development

Annual education budget for conferences, courses, and certifications. Access to online learning platforms and internal training programs.

Commuter and Transportation Benefits

Pre-tax commuter benefits for public transportation or parking. Support for remote work setups and necessary equipment.

Collaborative Office Environment

Access to modern office facilities in San Francisco with collaborative spaces designed for innovation and cross-team interaction.

Company Events and Culture

Regular team gatherings, company-wide events, and social activities fostering strong community and connection among employees.

401(k) Retirement Plan

Employer-matched retirement savings plan supporting your long-term financial security.

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OpenAI

OpenAI

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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
PythonTypeScript/JavaScriptGoSQL
Frameworks
PyTorchFlask/FastAPIRayKubernetes
Databases
PostgreSQLRedisBigQuery or SnowflakeVector Databases (Pinecone, Weaviate)
Tools
Kubernetes and Container OrchestrationPrometheus and GrafanaDatadog or New RelicGit and GitHubTerraform or Infrastructure-as-CodeCI/CD Systems (Jenkins, GitLab CI, GitHub Actions)
Other
Model Serving InfrastructureExperiment Management (Weights & Biases, MLflow)Feature Engineering and Feature StoresObservability and Logging (ELK Stack, Loki)Safety and Guardrail Systems

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