OpenAI

Platform Engineer, Forward Deployed Engineering (FDE) - NYC

OpenAI5 months ago
Location

New York City

Type

Full Time

Salary

USD 230,000 – 385,000

Level

Senior

Role

Platform Engineer

Posted

Feb 23, 2026

Full TimeSenior

The role

Summary

Platform Engineer at OpenAI's Forward Deployed Engineering org, responsible for building new platform capabilities from scratch while embedded with customer teams. This role combines hands-on software and ML engineering with cross-functional collaboration to transform customer signals into generalized platform features that scale across deployments. Ideal for senior engineers with 5+ years of experience shipping 0→1 products who excel in high-ambiguity environments and can bridge technical architecture with business outcomes.

What you'll do

Embed with Customer-Tagged FDE Teams: Provide hands-on technical leverage to customer-focused FDE pods, directly contributing to architecture decisions, product shaping, code refactoring, and feature implementation while maintaining the pod's ownership of customer relationships and day-to-day execution responsibilities.
Identify and Validate Cross-Customer Patterns: Translate recurring signals and requirements from multiple customer deployments into structured platform hypotheses with clear success criteria, scoped deliverables, and validation plans that align with real customer constraints and deployment timelines.
Drive Platform Standardization and Quality: Establish organization-wide engineering excellence through high-signal code reviews, pair programming mentorship, and development of lightweight tooling that makes architectural best practices, code readability, and correctness the organizational default across the FDE group.
Lead Cross-Functional Platform Initiatives: Collaborate with B2B Platform teams, customer-facing engineers, operations, and business stakeholders to bring sophisticated platform capabilities and products to production, ensuring technical decisions align with go-to-market strategy and customer deployment reality.
Own End-to-End Platform Primitives: Act as the designated responsible individual (DRI) for high-leverage platform capabilities, managing full lifecycle from requirements gathering through production deployment, making architectural tradeoffs explicit, and integrating customer feedback early to maintain grounding in real-world deployments.
Establish Systems-Thinking Approaches: Transform ambiguous feedback, operational failures, and escalations into durable product requirements and reusable platform abstractions rather than one-off fixes, ensuring platform work compounds over time and benefits the entire organization.

What we look for

Technical

Platform Architecture and Design PatternsDemonstrated expertise in designing scalable platform abstractions, API design, and reusable components that serve multiple downstream teams and use cases without becoming overgeneralized or brittle.
Reliability, Security, and Governance ImplementationProven ability to design and implement systems incorporating permissions models (RBAC), audit trails, data access boundaries, safe rollout mechanisms, comprehensive observability, and incident-driven hardening for production systems.
Full-Stack Product DevelopmentEnd-to-end ownership experience spanning requirements definition, hypothesis testing, instrumentation and metrics, error analysis, iterative refinement of success criteria, and production adoption strategies for customer-adjacent technical work.
Software and ML Engineering ExcellenceStrong foundational capabilities in both traditional software engineering (system design, databases, distributed systems) and machine learning engineering (model evaluation, prompt engineering, eval frameworks) for building AI-native platform capabilities.
Cross-Functional Communication and InfluenceAbility to translate complex technical concepts and tradeoffs for engineering, product, go-to-market, and executive audiences; capacity to present platform bets credibly in customer conversations and influence architectural decisions through clear reasoning.

Education

Computer Science or Related FieldBachelor's degree in Computer Science, Software Engineering, Mathematics, Physics, or equivalent practical software engineering experience. Advanced degrees or bootcamp completion may substitute for formal credentials with strong demonstrated capabilities.

Experience

Shipping 0-to-1 Product CapabilitiesMinimum 5+ years of software or ML engineering experience with a track record of building new capabilities from conception through production that other engineers and customers depend upon in their workflows.
High-Ambiguity, Fast-Iteration EnvironmentsBackground working in startups, early-stage teams, or product-centric organizations where requirements shift rapidly, feedback loops are tight, and the ability to make good decisions with incomplete information is critical.
Customer-Centric Technical Problem SolvingExperience owning technical initiatives driven by customer feedback, translating business requirements into technical specifications, validating solutions with end users, and iterating based on production usage patterns.
Production System Operation and ImprovementHands-on experience building systems where reliability, security, and governance requirements shape architectural decisions from inception, including designing for observability, safe deployments, and incident response.

Skills

Required skills

Distributed Systems ArchitectureDeep understanding of building scalable, reliable distributed systems including load balancing, partitioning, replication, consensus mechanisms, and handling eventual consistency challenges at scale.
API Design and System AbstractionsExpertise in designing clean, extensible APIs and platform abstractions that balance generality with simplicity, with consideration for backward compatibility and graceful evolution as requirements change.
Observability and DebuggingProficiency instrumenting systems with comprehensive logging, metrics, traces, and dashboards; ability to diagnose production issues efficiently and establish monitoring that surfaces problems before customer impact.
Code Quality and Architectural ReviewDemonstrated ability to conduct high-signal code reviews, identify architectural debt, mentor junior engineers, and establish patterns that make good design and readability the default rather than exception.
Product Thinking and Requirements AnalysisCapacity to translate ambiguous customer needs into crisp technical requirements, establish success metrics upfront, design validation experiments, and iterate on the problem definition as understanding deepens.
Cross-Functional CollaborationExcellent communication across technical and non-technical stakeholders; ability to simplify complex architectural decisions into business impact terms, align diverse priorities, and maintain collaboration under deadline pressure.

Nice to have

Large Language Model ApplicationsExperience building production systems leveraging LLMs, including prompt engineering, fine-tuning strategies, eval frameworks, handling model behavior variability, and integrating AI capabilities into broader platform architectures.
Enterprise and B2B Platform ExperienceBackground building platform features for B2B contexts with multiple customer types, experience managing feature flags, multi-tenancy considerations, and deployment patterns for diverse enterprise infrastructure.
Infrastructure and DevOpsFamiliarity with cloud platforms (AWS, GCP, Azure), containerization (Docker, Kubernetes), infrastructure-as-code, and CI/CD pipeline design that enables rapid, safe iteration and reliable deployments.
ML Ops and Model ServingExperience with ML model deployment, serving frameworks, monitoring model performance in production, A/B testing strategies for model versions, and handling data pipelines that feed ML systems.
Security and ComplianceKnowledge of application security principles (authentication, authorization, encryption), audit logging requirements, data residency and privacy considerations, and designing systems that support security and compliance requirements.
Technical Leadership and MentorshipTrack record of elevating team capability through mentorship, establishing coding standards, running effective design reviews, and creating psychological safety for technical discussions and constructive debate.

Compensation & benefits

Salary

USD 230,000 – 385,000 (annual)

Stock options

Available

Benefits

Equity Compensation

Competitive equity packages with standard vesting schedules; equity represents meaningful ownership stake and aligns incentives with long-term company success and valuation growth.

Health and Wellness Benefits

Comprehensive medical, dental, and vision insurance with competitive plans; mental health support, wellness programs, and fitness benefits to support overall wellbeing.

Flexible Work Arrangement

Hybrid work model with 3 days per week in-office requirement at San Francisco or New York locations, providing flexibility for focus work while maintaining strong team collaboration and culture.

Relocation Assistance

Comprehensive relocation support for candidates relocating to San Francisco or New York, including moving assistance and guidance for transitioning to new locations.

Unlimited Paid Time Off

Flexible time-off policy allowing unlimited vacation days, recognized sick leave, and personal days; encourages healthy work-life balance and recognizes employee autonomy.

Professional Development

Learning and development opportunities including conference attendance, technical training, and education reimbursement to support continuous skill development in rapidly evolving AI and platform engineering.

Parental and Family Support

Comprehensive parental leave programs, family planning support, and childcare benefits recognizing diverse employee needs and life circumstances.

Optional Travel Flexibility

Project-based travel (typically less than 10% annually) is optional, with occasional spikes for critical customer embeds or product launches; minimal recurring travel commitments.

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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
FastAPI/FlaskReactNext.jsLangChain/LlamaIndex
Databases
PostgreSQLRedisVector Databases (Pinecone/Weaviate)Cloud-Native Databases
Tools
Docker and KubernetesGit and GitHubOpenAI APIs and ModelsMonitoring and Observability StackCI/CD PlatformsNotebook Environments
Other
Prompt Engineering and LLM EvaluationSystem Design and ScalabilityData Privacy and Security ArchitectureA/B Testing and ExperimentationIncident Response and Post-Mortems

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