Platform Engineer, Forward Deployed Engineering (FDE)

Details

OpenAI’s Forward Deployed Engineering (FDE) organization operates at the intersection of product, engineering, research, and go-to-market. The FDE Platform team helps scale the organization’s impact across OpenAI’s platforms and products by embedding with customer-tagged FDE pods to support architecture, product shaping, refactoring, implementation, and reusable abstractions.

The Platform Engineer role is for software and ML engineers who want to build new platform capabilities from scratch based on real customer deployments. You will partner with customer-tagged FDEs, B2B Platform teams, and long-term product owners to determine what should be generalized, what should remain customer-specific, and what is ready for handoff.

This role is based in San Francisco or New York and follows a hybrid work model with three days in the office per week. The role does not require travel; travel is optional by project and typically less than 10%, with occasional increases for key deployments or launches.

Responsibilities

• Embed with customer-tagged FDE teams to support generalization through architecture, product shaping, refactoring, and implementation.
• Translate cross-customer patterns into platform hypotheses with clear success criteria, scope, and validation plans that account for real customer constraints.
• Establish engineering quality standards through code review and pairing.
• Build lightweight developer tooling that promotes sound architecture, readability, and correctness across FDE.
• Collaborate with B2B Product, customer-tagged FDEs, operations, and business partners to bring products and platform capabilities to market.
• Lead complex platform capabilities end to end when needed, acting as the directly responsible individual from requirements through implementation.
• Make key technical tradeoffs explicit and involve customer pods early to keep platform work grounded in real deployments.

Requirements

• 5+ years of software engineering or ML engineering experience.
• A track record of shipping zero-to-one capabilities that other engineers or customers depend on.
• Experience with customer-adjacent technical work, from scoping and hypothesis-setting through production adoption.
• Experience improving outcomes through structured iteration, including instrumentation, evaluations, error analysis, and progressively tighter success criteria.
• Experience building or operating systems where reliability, security, and governance materially influence design, including permissions and RBAC, auditability, data access boundaries, rollout safety, observability, and incident-driven hardening.
• Clear communication skills across engineering, product, go-to-market, and executive audiences.
• Ability to translate technical tradeoffs into adoption impact, sequencing decisions, and measurable outcomes.
• A systems-thinking approach that turns ambiguous feedback, failures, and escalations into product requirements and reusable platform capabilities rather than one-off fixes.
• Experience in high-ambiguity, fast-iteration environments such as startups or product-centric teams is a plus.

Benefits

• Base salary of $230,000–$385,000 per year.
• Equity, performance-related bonuses for eligible employees, and additional benefits.
• Medical, dental, and vision insurance, with employer contributions to Health Savings Accounts.
• Pre-tax accounts for health, dependent care, and commuter expenses.
• 401(k) retirement plan with employer match.
• Paid parental, medical, and caregiver leave.
• Paid time off, company holidays, and paid office closures.
• Mental health and wellness support.
• Employer-paid basic life and disability coverage.
• Annual learning and development stipend.
• Daily office meals and eligible meal delivery credits.
• Relocation support for eligible employees.
• Additional taxable fringe benefits may include charitable donation matching and wellness stipends.