Forward Deployed Engineer (FDE), Financial Services- NYC

OpenAI•New York, NY

3d•$185,000 - $300,000•Hybrid

About The Position

OpenAI’s Forward Deployed Engineering team partners with financial institutions to deploy production-grade AI systems in high-stakes environments. The team embeds with customers to translate frontier model capabilities into reliable, auditable systems, turning early deployments into repeatable solution patterns, operating standards, and evaluation practices that scale across regulated financial institutions. The Forward Deployed Engineer leads end-to-end deployments of OpenAI models within financial services organizations, where correctness, latency, explainability, and control are critical. The role translates complex workflows, data constraints, and regulatory requirements into production systems, and measures success through production adoption, workflow efficiency, risk reduction, revenue impact, and evaluation-driven feedback. The engineer works closely with Product, Research, GTM, Security, Legal, and GRC, and helps shape OpenAI’s Financial Services offering by identifying valuable use cases, defining solution patterns, and establishing repeatable deployments. The role is based in New York under a hybrid work model requiring three office days per week. Relocation assistance is offered, and travel up to 50% may be required.

Requirements

At least 5 years of software engineering, ML engineering, or technical deployment experience with customer-facing ownership in financial services or adjacent regulated industries.
Experience owning complex AI or data-driven systems end-to-end, from scoping through production adoption, where errors carry financial or regulatory consequences.
Experience with financial workflows such as research, trading, risk, compliance, underwriting, operations, or investment processes, and understanding of how incentives and controls shape adoption.
Ability to write and review production-grade code across backend and frontend systems using Python, JavaScript, or comparable stacks.
Experience deploying systems powered by LLMs or generative models, with understanding of how model behavior, evaluation, and guardrails affect user trust and business outcomes.
Ability to communicate clearly across engineering, product, risk, compliance, and executive stakeholders, translating technical trade-offs into decision quality and risk posture.
Systems thinking and high execution standards, including turning incidents, escalations, and failures into improved operating standards and repeatable deployment patterns.
Ability to stay calm and decisive when delivery is urgent, ambiguous, and high-stakes.

Responsibilities

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, such as accuracy, latency, coverage, and 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.