Description
OpenAI's Forward Deployed Engineering team partners with customers to turn research breakthroughs into production systems. 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.
Responsibilities:
- 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.
- 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.
Requirements:
- 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 behavior 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.