About the roleWe are hiring a Forward Deployed Engineer (FDE) to lead end-to-end deployments of OpenAI's models inside financial services organizations where correctness, latency, explainability, and control matter. You will work with customers who are experts in investment banking, trading, risk, compliance, underwriting, research, operations, or investment decision-making, translating complex workflows, data constraints, and regulatory requirements into production systems.
You will measure success through production adoption, workflow efficiency, risk reduction, revenue impact, and evaluation-driven feedback loops that inform product, model, and GTM strategy. You'll work closely with Product, Research, GTM, Security, Legal, and GRC to deliver systems that meet enterprise standards for governance, auditability, and operational resilience.
You will also play a central role in shaping OpenAI's Financial Services offering - identifying high-value use cases, defining solution patterns, and building the first repeatable deployments that scale across institutions. Learn more about some of our work with financial institutions.
This role is based in New York. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. Travel up to 50% may be required.
In this role, you will- Design and ship production AI systems around models, owning integrations, data flows, reliability, observability, and on-call readiness across financial workflows.
- Lead discovery and scoping from pre-sales through post-production, translating ambiguous business problems into hypothesis-driven problem framing, system requirements, and delivery plans with measurable outcomes.
- Define and enforce launch criteria for regulated financial environments, including controls, audit artifacts, evaluation benchmarks, and acceptance thresholds tied to risk and performance.
- Build in sensitive data environments where access controls, data lineage, explainability, and failure modes shape architecture and operating procedures.
- Run evaluation loops that measure model and system quality against workflow-specific financial benchmarks (e.g., accuracy, latency, coverage, false positives) and use results to drive iteration.
- Own delivery state across multiple workstreams, making trade-offs between scope, speed, and quality to protect production outcomes.
- Distill deployment learnings into hardened primitives, reference architectures, playbooks, and tooling that scale across financial institutions.
- Surface field feedback that informs model behavior, product capabilities, and platform gaps in real-world financial use cases.
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 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.
- Have experience with financial workflows such as research, trading, risk, compliance, underwriting, operations, or investment processes, and understand how incentives and controls shape adoption.
- 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.
This role is based in New York. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. Travel up to 50% may be required.
