Description
Compensation
The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The salary range for this role is $162K – $280K, with generous equity, performance-related bonuses for eligible employees, and the following benefits.
Benefits include:
- Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
- Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
- 401(k) retirement plan with employer match
- Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
- Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
- 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time
- Mental health and wellness support
- Employer-paid basic life and disability coverage
- Annual learning and development stipend to fuel your professional growth
- Daily meals in our offices, and meal delivery credits as eligible
- Relocation support for eligible employees
- Additional taxable fringe benefits, such as charitable donation matching and wellness stipends
About the team
The Forward Deployed Engineering team at OpenAI partners with customers to turn research breakthroughs into production systems. They embed deeply with users to solve high-leverage problems, moving quickly from prototype to deployment.
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.
Responsibilities include:
- Embedding with law firms and legal teams to understand their workflows, identify high-value opportunities, and select the right initial use cases to prove value
- Building full-stack systems that deliver customer value and sharpen how we learn
- Working closely with customer teams, understanding their needs, and guiding adoption of what you build
- Making trade-offs between scope, speed, and quality; adjusting plans to protect delivery
- Contributing directly in the code when progress or clarity depends on it
- Codifying working patterns into tools, playbooks, or building blocks that others can use
- Sharing field feedback that helps Research and Product understand where the models succeed and where they can improve
Requirements
You might thrive in this role if you:
- Have 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