Forward Deployed Engineer (FDE), Legal-SF

About the Role

We are hiring a Forward Deployed Engineer (FDE) to build and deploy AI systems for legal work. You will embed deeply with customers-often working closely with senior legal practitioners-to identify the right first use case, rapidly prototype a solution, and prove measurable value. Early engagements may focus on creating a “hero” workflow that shows how OpenAI’s models can support legal analysis, drafting, research, or real-time work with complex case records.

You will own the technical work from discovery and proof of concept through production adoption, while translating customer workflows and constraints into clear product feedback. As the motion matures, you will turn successful deployments into repeatable patterns that can scale across larger accounts and the broader legal market.

This role is based in San Francisco or New York City. We use a hybrid work model of three days in the office per week and offer relocation assistance. Travel up to 50% may be required.

In This Role, You Will

• Embed with law firms and legal teams to understand their workflows, identify high-value opportunities, and select the right initial use cases to prove value.
• Build full-stack systems that deliver customer value and sharpen how we learn
• Embed closely with customer teams, understand their needs, and guide adoption of what you build
• Make trade-offs between scope, speed, and quality; adjust plans to protect delivery
• Contribute directly in the code when progress or clarity depends on it
• Codify working patterns into tools, playbooks, or building blocks that others can use
• Share field feedback that helps Research and Product understand where the models succeed and where they can improve

You Might Thrive in This Role If You

• Bring 5+ years of software engineering, ML engineering, or technical deployment experience with customer-facing ownership in financial services or adjacent regulated industries.
• Have experience with legal technology, legal services, compliance-heavy workflows, or professional services; this is helpful but not required.
• Have owned complex AI or data-driven systems end-to-end, from scoping through production adoption, in environments where errors carry real financial or regulatory consequences.
• Write and review production-grade code across backend and frontend systems using Python, JavaScript, or comparable stacks.
• Have deployed systems powered by LLMs or generative models and understand how model behavior, evaluation, and guardrails affect user trust and business outcomes.
• Communicate clearly across engineering, product, risk, compliance, and executive stakeholders, translating technical trade-offs into decision quality and risk posture.
• Apply systems thinking with high execution standards, turning incidents, escalations, and failures into improved operating standards and repeatable deployment patterns.
• Stay calm and decisive when delivery is urgent, ambiguous, and high-stakes.