Forward Deployed Engineer (FDE), Financial Services- NYC

OpenAIs Forward Deployed Engineering team partners with banks asset managers insurers and private capital firms to deploy production-grade AI systems in high-stakes financial environments. We operate at the intersection of customer delivery and core platform development embedding deeply with customers to translate frontier model capabilities into reliable auditable systems that create measurable business impact. Our work turns early deployments into repeatable solution patterns operating standards and evaluation practices that scale across regulated financial institutions.

We are hiring a Forward Deployed Engineer (FDE) to lead end-to-end deployments of OpenAIs 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. Youll 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 OpenAIs 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.

• 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.

• 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.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core and to achieve our mission we must encompass and value the many different perspectives voices and experiences that form the full spectrum of humanity.

We are an equal opportunity employer and we do not discriminate on the basis of race religion color national origin sex sexual orientation age veteran status disability genetic information or other applicable legally protected characteristic.

For additional information please see OpenAIs Affirmative Action and Equal Employment Opportunity Policy Statement.

Background checks for applicants will be administered in accordance with applicable law and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws including the San Francisco Fair Chance Ordinance the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct adverse and negative relationship with the following job duties potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary confidential and non-public addition job duties require access to secure and protected information technology systems and related data security obligations.

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