# Forward Deployed Engineer (FDE), Financial Services- NYC
**Company:** [OpenAI](https://scaleengineer.com/companies/openai)
Lead end-to-end deployments of OpenAI's frontier AI models in high-stakes financial services environments, translating complex workflows and regulatory requirements into production-grade systems. As a Forward Deployed Engineer, you'll partner with banks, asset managers, and investment firms to embed deeply with customers, measuring success through production adoption, efficiency gains, and risk reduction. This role combines customer delivery expertise with core platform development, requiring 5+ years of software or ML engineering experience in regulated industries with demonstrated ability to own complex systems from scoping through production.
**Role:** Forward Deployed Engineer
**Seniority:** Senior
**Locations:** New York City
**Salary:** 185000–300000 USD
[Apply](https://jobs.ashbyhq.com/openai/7f76be3a-38d0-4ff4-b997-9f1672e78bc0)
Canonical: https://scaleengineer.com/jobs/openai/forward-deployed-engineer-fde-financial-services-nyc
---
## Responsibilities

- Design and Deploy Production AI Systems: Design and ship production-grade AI systems around large language models, owning end-to-end integrations, data flows, reliability engineering, observability infrastructure, and on-call readiness across financial workflows. Ensure systems meet enterprise standards for governance, auditability, and operational resilience in regulated environments.
- Lead Discovery and Scoping Across Sales Cycle: Lead comprehensive discovery and scoping from pre-sales through post-production phases, translating ambiguous business problems into hypothesis-driven problem framing, detailed system requirements, and delivery plans with measurable outcomes. Work with customers who are experts in investment banking, trading, risk, compliance, underwriting, and investment decision-making.
- Define Launch Criteria for Regulated Environments: Define and enforce launch criteria for regulated financial services environments, including access controls, audit artifacts, evaluation benchmarks, and acceptance thresholds tied to risk and performance. Ensure systems meet financial industry compliance requirements and governance standards.
- Build Systems in Sensitive Data Environments: Build sophisticated systems in sensitive financial data environments where access controls, data lineage, explainability, and failure modes fundamentally shape architecture and operating procedures. Implement guardrails that protect customer data while maintaining system performance.
- Run Evaluation Loops and Benchmarking: Own and run comprehensive evaluation loops that measure model quality and system performance against workflow-specific financial benchmarks including accuracy, latency, coverage, and false positive rates. Use evaluation results to drive rapid iteration and continuous improvement of deployed systems.
- Own Multi-Workstream Delivery State: Own delivery state and progress across multiple concurrent workstreams, making strategic trade-offs between scope, speed, and quality to protect production outcomes. Manage delivery complexity and coordinate across product, research, GTM, security, legal, and governance teams.
- Distill Learnings into Scalable Patterns: Distill deployment learnings into hardened primitives, reference architectures, playbooks, and tooling that enable repeatable solutions and scale across multiple financial institutions. Create institutional knowledge that accelerates future deployments.
- Surface Field Feedback to Product Teams: Surface field feedback and customer insights from real-world deployments that inform model behavior, product capabilities, platform gaps, and GTM strategy. Act as the bridge between customer needs and OpenAI's core product and research teams.

## Requirements

### education

- {"name":"Computer Science or Related Technical Discipline","description":"Bachelor's degree in Computer Science, Mathematics, Physics, Engineering, or equivalent professional experience. Advanced degree or specialized certifications in machine learning, distributed systems, or financial technology valued."}

### technical

- {"name":"Production Python Development","description":"Strong proficiency in writing and reviewing production-grade Python code across backend systems, including experience with frameworks, async patterns, dependency management, and operational concerns like logging and monitoring."}
- {"name":"Full-Stack Web Development","description":"Competency in JavaScript or TypeScript for frontend and backend systems development. Experience building user interfaces that help financial professionals understand and audit AI system decisions."}
- {"name":"API Design and Integration","description":"Design and implement robust REST or GraphQL APIs that integrate with customer systems, including authentication, rate limiting, versioning, and documentation for enterprise integration patterns."}
- {"name":"Data Engineering and Pipelines","description":"Experience building and maintaining data pipelines, data validation, ETL processes, and data warehousing solutions. Understanding of data quality, lineage tracking, and governance in regulated environments."}
- {"name":"Machine Learning Systems Architecture","description":"Understanding of ML model serving, inference optimization, model governance, versioning, and monitoring in production. Experience with model evaluation frameworks, A/B testing, and continuous model improvement."}
- {"name":"Database Technologies","description":"Proficiency with both SQL databases and NoSQL solutions. Experience optimizing queries, designing schemas for financial data, and implementing backup and disaster recovery strategies."}
- {"name":"Cloud Infrastructure and DevOps","description":"Hands-on experience with cloud platforms like AWS, Google Cloud, or Azure. Knowledge of containerization, orchestration, infrastructure-as-code, CI/CD pipelines, and operational monitoring."}
- {"name":"Security and Compliance Engineering","description":"Understanding of enterprise security practices, data encryption, access control models, audit logging, SOC 2 compliance, and financial services regulatory requirements like MiFID II or Dodd-Frank."}

### experience

- {"name":"5+ Years Software or ML Engineering Experience","description":"Demonstrate 5 or more years of professional software engineering, ML engineering, or technical deployment experience with direct customer-facing ownership in financial services or adjacent regulated industries such as healthcare or telecommunications."}
- {"name":"End-to-End Complex Systems Ownership","description":"Proven track record owning complex AI or data-driven systems end-to-end, from initial scoping and design through production adoption and ongoing operations, in environments where errors carry real financial or regulatory consequences."}
- {"name":"Financial Services Domain Knowledge","description":"Hands-on experience with financial workflows such as research, trading, risk management, compliance, underwriting, operations, or investment processes. Understand how institutional incentives, controls, and stakeholder concerns shape adoption and business impact."}
- {"name":"LLM or Generative Model Deployment","description":"Demonstrated experience deploying systems powered by large language models or generative models in production environments. Understand how model behavior, evaluation methodology, and guardrails affect user trust, adoption, and business outcomes."}
- {"name":"Cross-Functional Stakeholder Management","description":"Track record of communicating clearly and effectively across engineering, product, risk, compliance, and executive stakeholder groups. Ability to translate technical trade-offs, risk posture, and architectural decisions into language that drives organizational decision-making."}

## Skills

### required

- {"name":"Large Language Model (LLM) Application Development","description":"Hands-on experience building production applications with large language models, including prompt engineering, fine-tuning strategies, guardrails, and mitigation strategies for hallucinations and model failures in high-stakes environments."}
- {"name":"System Design and Architecture","description":"Ability to design end-to-end systems that span frontend, backend, data infrastructure, and ML components. Experience making architectural trade-offs considering reliability, latency, scalability, and operational complexity."}
- {"name":"Production Debugging and Observability","description":"Expert-level debugging skills and experience building comprehensive observability solutions including logging, metrics, tracing, and alerting. Ability to diagnose and resolve issues in production environments under pressure."}
- {"name":"Deployment and Delivery Excellence","description":"Track record of shipping complex systems on aggressive timelines while maintaining quality and reliability. Experience with deployment strategies, canary releases, rollback procedures, and incident response."}
- {"name":"Regulatory and Compliance Understanding","description":"Knowledge of financial services regulations, compliance frameworks, audit requirements, and governance standards. Experience implementing systems that satisfy regulatory oversight and auditability requirements."}
- {"name":"Customer Discovery and Requirements Gathering","description":"Ability to lead technical discovery sessions, translate vague customer problems into concrete requirements, and scope feasible solutions. Experience managing customer expectations and communicating timelines."}
- {"name":"Evaluation and Benchmarking","description":"Experience designing evaluation frameworks, creating benchmark datasets, and running rigorous tests to measure system quality. Understanding of statistical significance, bias detection, and performance analysis."}
- {"name":"Technical Leadership and Influence","description":"Ability to influence technical decisions across teams without direct authority. Experience mentoring junior engineers, conducting code reviews, and setting engineering standards."}

### preferred

- {"name":"Investment Banking or Capital Markets Experience","description":"Background in investment banking, trading, risk management, or other capital markets functions provides valuable context for understanding financial workflows and customer pain points."}
- {"name":"Fintech Startup Experience","description":"Prior experience at fintech companies or FinTech-focused divisions of larger institutions, bringing knowledge of modern financial technology stacks and industry patterns."}
- {"name":"Enterprise Software Sales Engineering","description":"Background in presales engineering or solutions architecture for enterprise software, with experience demonstrating value, addressing concerns, and closing technical evaluations."}
- {"name":"Open-Source Contributions","description":"Active contributions to open-source projects related to machine learning, data engineering, or backend systems demonstrate commitment to engineering excellence and community engagement."}
- {"name":"Advanced Database Optimization","description":"Deep expertise in query optimization, database tuning, and handling high-volume transactional systems that require sub-millisecond latency for time-sensitive financial operations."}
- {"name":"API Security and Authentication","description":"Specialized knowledge in OAuth 2.0, SAML, mutual TLS, and other enterprise authentication standards essential for secure financial services integration."}
- {"name":"Model Risk Management","description":"Familiarity with model risk management frameworks, model governance, independent validation processes, and regulatory guidance on AI/ML model governance."}
- {"name":"Real-time Systems and Low-Latency Architecture","description":"Experience designing systems with strict latency constraints, event-driven architectures, and real-time data streaming required for trading and risk applications."}

## Tech stack

### tools

- {"name":"AWS Services (EC2, S3, Lambda, RDS, SageMaker)","description":"Primary cloud infrastructure platform for deploying and scaling AI systems, managing data storage, and leveraging managed AI/ML services."}
- {"name":"Docker and Kubernetes","description":"Containerization and orchestration technologies essential for deploying, scaling, and managing microservices in production financial environments."}
- {"name":"Git and GitHub/GitLab","description":"Version control and collaboration platform for code management, code review workflows, and CI/CD integration in team environments."}
- {"name":"Prometheus and Grafana","description":"Monitoring and observability stack for tracking system metrics, alerting on anomalies, and maintaining visibility into production AI system performance."}
- {"name":"Jenkins or GitHub Actions","description":"CI/CD platforms for automating testing, building, and deployment pipelines to ensure code quality and deployment reliability."}
- {"name":"Jupyter Notebooks","description":"Development environment for data exploration, model evaluation, and creating documentation for financial analysis and model behavior."}
- {"name":"PagerDuty or similar On-Call Management","description":"Incident response and on-call management platform for coordinating emergency response to production incidents in 24/7 operational environments."}

### others

- {"name":"Retrieval-Augmented Generation (RAG)","description":"Technique for grounding LLM responses in external data sources and documents, critical for financial use cases requiring factual accuracy and auditability."}
- {"name":"Model Evaluation Frameworks","description":"Methodologies for rigorously testing and validating model performance, including benchmark creation, statistical analysis, and bias detection for financial applications."}
- {"name":"API Security Standards","description":"OAuth 2.0, SAML, mutual TLS, and encryption standards required for secure integration with enterprise financial systems and regulatory compliance."}
- {"name":"Data Lineage and Governance","description":"Tools and practices for tracking data provenance, maintaining audit trails, and ensuring data quality in regulated financial environments."}
- {"name":"Model Serving and Inference Optimization","description":"Techniques for deploying trained models to production, optimizing inference latency, managing model versions, and implementing model governance."}
- {"name":"Incident Response and Post-Mortems","description":"Structured approaches to incident investigation, root cause analysis, and learning systems that improve operational resilience and prevent recurrence."}

### databases

- {"name":"PostgreSQL","description":"Enterprise-grade relational database for financial transactions and structured data. Strong ACID guarantees and compliance with financial systems requirements."}
- {"name":"Redis","description":"In-memory data store for caching, session management, and real-time data access patterns required by trading and risk systems needing sub-millisecond latency."}
- {"name":"Elasticsearch","description":"Search and analytics engine for querying large volumes of financial data, audit logs, and transaction records with full-text search capabilities."}
- {"name":"MongoDB or DynamoDB","description":"NoSQL solutions for flexible schema requirements, document storage, and horizontal scaling when handling varied financial data types and volumes."}

### languages

- {"name":"Python","description":"Primary backend language for building AI system integrations, data pipelines, and API services. Required for ML model serving, data processing, and backend infrastructure."}
- {"name":"JavaScript/TypeScript","description":"Essential for frontend development and full-stack application building. TypeScript strongly preferred for type safety in complex financial applications."}
- {"name":"SQL","description":"Core language for data querying, analysis, and management in relational databases. Must be proficient in complex queries, optimization, and financial data modeling."}
- {"name":"Bash/Shell Scripting","description":"Used for DevOps automation, deployment scripts, infrastructure management, and operational tooling in cloud environments."}

### frameworks

- {"name":"FastAPI or Flask","description":"Modern Python web frameworks for building high-performance REST APIs. FastAPI preferred for async support and automatic documentation generation."}
- {"name":"React or Vue.js","description":"Frontend frameworks for building user interfaces that allow financial professionals to interact with AI systems, review decisions, and audit model behavior."}
- {"name":"LangChain or LlamaIndex","description":"Popular frameworks for building LLM applications, including retrieval-augmented generation (RAG), prompt orchestration, and chain composition for financial workflows."}
- {"name":"Django","description":"Alternative Python framework offering comprehensive tooling for larger applications requiring ORM, authentication, and admin functionality for enterprise financial systems."}

## Benefits

### benefits

## Compensation

- **max:** 0
- **min:** 0
- **currency:** 
- **stockOptions:** false

## Interview process

### steps

## Full description
## About the team

OpenAI’s 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.

## About the role

We 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](https://openai.com/index/introducing-chatgpt-financial-services/).

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.

**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 [OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement](https://cdn.openai.com/policies/eeo-policy-statement.pdf).

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 information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.

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At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.