Forward Deployed Engineer (FDE) - AI Infra at OpenAI

Job description

About the team OpenAI’s Forward Deployed Engineering team partners with customers to turn research breakthroughs into production systems. We operate at the intersection of customer delivery and core platform development. About the role Forward Deployed Engineers lead complex deployments of frontier models in production. You will embed with customers where model performance matters, delivery is urgent, and ambiguity is the default. You will use this to map their problems, structure delivery, and ship fast. As a Forward Deployed Engineer for AI Infra, you will work directly with customer engineers, researchers and product engineers on optimization problems across semiconductors and AI infrastructure. You will write and debug software, run experiments against real workloads and hardware, and improve the tools and product used across engagements. You will help define the problem and success criteria, build the software needed to test an approach, and stay with the work through validation and deployment. Work may include inference performance and chip-design optimization. Success means producing results that stand up to correctness and performance checks, and contributing improvements that other engagements can use. Success in this role means owning the delivery state across workstreams. You will hold the bar on quality and pace and help OpenAI learn through execution. This role is based in San Francisco. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. Travel up to 50% is required. In this role, you will: - Own technical delivery for agreed semiconductor and AI-infrastructure workstreams, from initial scoping and prototypes through validation, production deployment and adoption. - Work closely with customer engineers to understand their workflows and constraints, define workloads, baselines and success criteria, and help what you build become part of how they work. - Scope and sequence delivery, identify dependencies and blockers early, and make practical trade-offs between scope, speed and quality as the work develops. - Personally build and debug production-quality software, including harnesses, integrations and product features that apply frontier models to problems in chip design, verification, inference and performance engineering. - Design reproducible experiments, evaluations, graders and benchmarks to test approaches, validate correctness and performance, and establish whether improvements hold up in real engineering environments. - Investigate model behavior and failure modes alongside researchers, contributing evaluation or training data and targeted experiments where they can improve the underlying capability. - Contribute directly to core product development, working with product engineers to resolve tooling gaps, ship useful improvements and bring field learning into product decisions. - Lead technical discussions and maintain alignment across customer, Product and Research teams. Build the domain and product fluency to explain capabilities, limitations and results, and keep ownership, risks and next steps clear. - Turn successful approaches into reusable software, tools and playbooks, reducing the effort needed for future engagements and helping customer teams operate what has been built. You might thrive in this role if you: - Bring 5+ years of engineering or technical deployment experience that includes customer-facing work - Have scoped and delivered complex systems in fast-moving or ambiguous environments - Write and review production-grade software, and debug unfamiliar systems across code, integrations and runtime behavior. - Have built or deployed systems powered by LLMs or generative models and understand how model behaviour affects product experience - Simplify complexity and make fast, sound decisions under pressure - Communicate clearly with engineers, product teams, and customer stakeholders - Spot risks early and adjust without slowing down - Model calm and judgment when the stakes are high - Have a strong product sense and have contributed towards productizing efforts - Have hands-on experience with applied machine learning, model evaluation, data quality, experimentation, or model-behavior analysis - Can turn an ambiguous technical problem into a clear evaluation, a credible baseline, and a reproducible experiment - Are comfortable learning unfamiliar technical domains and working closely with researchers, product engineers and customer engineers. Experience in semiconductor development, data-center systems or inference engineering is helpful. 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 cdn.openai.com. 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. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form form.asana.com. No response will be provided to inquiries unrelated to job posting compliance. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link form.asana.com. OpenAI Global Applicant Privacy Policy cdn.openai.com 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.

Requirements

Must-haves

About the team
OpenAI’s Forward Deployed Engineering team partners with customers to turn research breakthroughs into production systems. We operate at the intersection of customer delivery and core platform development.
Success in this role means owning the delivery state across workstreams. You will hold the bar on quality and pace and help OpenAI learn through execution.
Own technical delivery for agreed semiconductor and AI-infrastructure workstreams, from initial scoping and prototypes through validation, production deployment and adoption.
Work closely with customer engineers to understand their workflows and constraints, define workloads, baselines and success criteria, and help what you build become part of how they work.
Scope and sequence delivery, identify dependencies and blockers early, and make practical trade-offs between scope, speed and quality as the work develops.
Personally build and debug production-quality software, including harnesses, integrations and product features that apply frontier models to problems in chip design, verification, inference and performance engineering.
Design reproducible experiments, evaluations, graders and benchmarks to test approaches, validate correctness and performance, and establish whether improvements hold up in real engineering environments.
Investigate model behavior and failure modes alongside researchers, contributing evaluation or training data and targeted experiments where they can improve the underlying capability.
Contribute directly to core product development, working with product engineers to resolve tooling gaps, ship useful improvements and bring field learning into product decisions.
Turn successful approaches into reusable software, tools and playbooks, reducing the effort needed for future engagements and helping customer teams operate what has been built.
You might thrive in this role if you:
Bring 5+ years of engineering or technical deployment experience that includes customer-facing work
Have scoped and delivered complex systems in fast-moving or ambiguous environments
Write and review production-grade software, and debug unfamiliar systems across code, integrations and runtime behavior.
Have built or deployed systems powered by LLMs or generative models and understand how model behaviour affects product experience
Simplify complexity and make fast, sound decisions under pressure
Communicate clearly with engineers, product teams, and customer stakeholders
Spot risks early and adjust without slowing down
Model calm and judgment when the stakes are high
Have a strong product sense and have contributed towards productizing efforts
Have hands-on experience with applied machine learning, model evaluation, data quality, experimentation, or model-behavior analysis
Can turn an ambiguous technical problem into a clear evaluation, a credible baseline, and a reproducible experiment

Nice-to-haves

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Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learning

Platform: ashby
Effort: quick