# Forward Deployed Engineer (FDE) - AI Infra - SF
**Company:** [OpenAI](https://scaleengineer.com/companies/openai)
Join OpenAI's Forward Deployed Engineering team to lead complex production deployments of frontier AI models at the intersection of customer delivery and core platform development. As an AI Infrastructure Forward Deployed Engineer, you will embed with enterprise customers to optimize model performance, solve inference and chip-design challenges, and ship production systems in high-stakes, fast-moving environments. This role requires 5+ years of engineering experience with demonstrated expertise in distributed systems, machine learning deployment, and cross-functional leadership to drive technical delivery from prototype through production validation.
**Role:** Forward Deployed Engineer
**Seniority:** Senior
**Locations:** San Francisco
**Salary:** 185000–300000 USD
[Apply](https://jobs.ashbyhq.com/openai/7472e486-0aa6-4f9d-86da-2105a42b2dce)
Canonical: https://scaleengineer.com/jobs/openai/forward-deployed-engineer-fde-ai-infra-sf
---
## Responsibilities

- Lead Technical Delivery Across Infrastructure Workstreams: Own end-to-end technical delivery for semiconductor and AI-infrastructure projects, managing scoping, prototype development, validation testing, production deployment, and adoption phases. Establish technical milestones, manage dependencies, and maintain quality standards while balancing scope, speed, and execution velocity in ambiguous environments.
- Partner with Customer Engineering Teams: Embed directly with customer engineers to understand their operational workflows, infrastructure constraints, and business requirements. Define workloads, establish baselines and success criteria, build rapport with technical stakeholders, and ensure deliverables integrate seamlessly into customer production systems.
- Build Production-Grade Software and Integrations: Develop high-quality software including test harnesses, inference optimization tools, performance profiling utilities, and product integrations that apply frontier models to semiconductor design verification, chip-level optimization, and inference engineering challenges. Debug complex systems spanning code, hardware integrations, and runtime behavior.
- Design Rigorous Experiments and Benchmarks: Create reproducible experimental frameworks, evaluation methodologies, custom graders, and performance benchmarks to validate model correctness and performance improvements in real production environments. Establish credible baselines and measure whether optimizations sustain benefits at customer scale.
- Investigate Model Behavior and Failure Modes: Collaborate with AI research teams to analyze model behavior under production conditions, identify failure modes, and contribute targeted evaluation datasets and experiments that improve underlying model capabilities. Translate field observations into research insights.
- Contribute to Core Product Development: Work directly with product engineering teams to identify tooling gaps, design and ship improvements that benefit multiple customer engagements, and bring field learning back into product strategy decisions. Reduce friction for future deployments through productized solutions.
- Drive Cross-Functional Alignment and Communication: Lead technical discussions across customer, product, and research teams. Build domain expertise and product fluency to clearly communicate model capabilities, limitations, and results. Maintain transparent ownership of risks, dependencies, and next steps across all stakeholders.
- Systematize and Operationalize Successful Approaches: Transform proven technical approaches into reusable software frameworks, operational playbooks, and best-practice documentation. Enable customer teams to operate and maintain deployed systems independently while reducing deployment effort for subsequent engagements.

## Requirements

### education

- {"name":"Bachelor's Degree in Computer Science or Equivalent","description":"Formal qualification in Computer Science, Computer Engineering, or related field, or equivalent practical engineering experience demonstrating mastery of computer architecture, systems design, and software development fundamentals."}

### technical

- {"name":"Distributed Systems and Infrastructure Design","description":"5+ years building, deploying, and debugging complex distributed systems including microservices architectures, data pipelines, and cloud infrastructure at scale. Experience with system design tradeoffs, performance optimization, and working across hardware and software boundaries."}
- {"name":"Machine Learning Deployment and Model Serving","description":"Hands-on experience deploying LLMs or generative AI models in production environments. Understanding of inference optimization, model serving frameworks, GPU/TPU utilization, batching strategies, and how model behavior directly impacts product performance and user experience."}
- {"name":"Production-Quality Software Development","description":"Demonstrated ability to write, review, and maintain production-grade code with strong fundamentals in debugging complex systems, code quality standards, testing strategies, and system reliability. Proficiency in multiple programming languages such as Python, C++, or Go."}
- {"name":"Experimental Design and Rigorous Evaluation","description":"Applied expertise in designing reproducible experiments, establishing baselines, creating evaluation frameworks, and statistical analysis. Ability to transform ambiguous technical problems into well-defined hypotheses, credible benchmarks, and measurable success criteria."}
- {"name":"AI Infrastructure and Performance Engineering","description":"Knowledge of modern AI infrastructure including GPU clusters, data center networking, inference engines, and optimization techniques. Understanding of performance profiling, bottleneck identification, and systems-level optimizations for machine learning workloads."}

### experience

- {"name":"Customer-Facing Technical Delivery","description":"5+ years in engineering or technical roles involving significant customer engagement. Proven track record managing stakeholder expectations, delivering complex solutions under time pressure, and turning customer requirements into technical specifications and working systems."}
- {"name":"Fast-Moving and Ambiguous Environments","description":"Demonstrated success scoping and delivering complex systems where requirements evolve, timelines are aggressive, and technical challenges are novel. Experience making sound decisions with incomplete information and maintaining execution pace while managing risks."}
- {"name":"Cross-Functional Technical Leadership","description":"Experience leading technical initiatives across multiple teams including engineers, product managers, researchers, and customer stakeholders. Strong ability to synthesize diverse perspectives, drive consensus, and maintain alignment on technical direction and delivery milestones."}
- {"name":"Semiconductor or Data Center Systems (Preferred)","description":"Background in semiconductor design, chip verification, data-center infrastructure, or inference optimization is highly valuable. Understanding of hardware-software co-optimization, compute architecture constraints, and performance characterization on real hardware."}

## Skills

### required

- {"name":"Python and Systems Programming","description":"Proficiency in Python for rapid prototyping and scripting, plus strong systems programming skills in C++ or Go for performance-critical code. Ability to write clean, maintainable code and debug complex runtime behavior."}
- {"name":"Inference Optimization and Performance Profiling","description":"Deep understanding of inference optimization techniques including quantization, pruning, batching, kernel fusion, and memory optimization. Hands-on experience with profiling tools and methodologies to identify performance bottlenecks."}
- {"name":"Data Center and GPU Systems Knowledge","description":"Practical knowledge of GPU architectures, CUDA programming concepts, distributed training and inference frameworks, and data center networking. Understanding of hardware constraints and how to design software for efficient GPU utilization."}
- {"name":"Experimental Rigor and Reproducibility","description":"Ability to design rigorous experiments with proper controls, establish and communicate baselines, use statistical methods to validate results, and create reproducible evaluation frameworks that withstand scrutiny."}
- {"name":"System-Level Debugging and Troubleshooting","description":"Expert-level debugging skills spanning application code, system libraries, integrations, and runtime behavior. Ability to diagnose issues in unfamiliar codebases and complex distributed systems under time pressure."}
- {"name":"Technical Communication and Stakeholder Management","description":"Clear communication of complex technical concepts to both technical engineers and non-technical stakeholders. Ability to explain trade-offs, manage expectations, and maintain alignment across diverse cross-functional teams."}

### preferred

- {"name":"Model Behavior Analysis and LLM Understanding","description":"Experience analyzing and improving LLM or generative model behavior including prompt engineering, fine-tuning evaluation, token utilization analysis, and understanding how model design choices affect inference performance and accuracy."}
- {"name":"Chip Design and Verification Tools","description":"Familiarity with semiconductor design workflows, hardware description languages, chip simulation, design verification processes, or experience using chip design optimization tools and understanding the constraints of hardware engineers."}
- {"name":"MLOps and Model Deployment Infrastructure","description":"Experience with MLOps practices, model serving platforms such as TensorRT, TensorFlow Serving, or vLLM, containerization with Docker and Kubernetes, and CI/CD pipelines for machine learning systems."}
- {"name":"Collaborative Research Engagement","description":"Experience working alongside research teams to translate academic insights into production systems. Ability to contribute evaluation data, design experiments that inform research direction, and bridge the research-to-product gap."}
- {"name":"Customer Success and Productization","description":"Track record of turning successful customer engagement solutions into productized offerings. Experience identifying patterns across customer engagements and building generalizable tools and frameworks."}

## Tech stack

### tools

- {"name":"NVIDIA Profiling Tools (Nsys, Ncu, Nccl-Tests)","description":"GPU profiling and performance analysis tools essential for identifying bottlenecks, measuring kernel efficiency, and optimizing inference workloads at scale."}
- {"name":"Benchmarking and Testing Frameworks","description":"Experience with custom benchmark development, load testing tools, and performance regression detection to validate production improvements."}
- {"name":"Version Control and Collaboration Tools","description":"Proficiency with Git, GitHub, and collaborative development workflows to manage code reviews, infrastructure as code, and cross-team coordination."}
- {"name":"Containerization and Orchestration","description":"Docker, Kubernetes, or similar technologies for packaging, deploying, and managing inference workloads across customer environments and development systems."}

### others

- {"name":"Distributed Systems Architecture","description":"Deep understanding of distributed system design patterns, consensus mechanisms, failure modes, and strategies for building reliable inference infrastructure at scale."}
- {"name":"Performance Characterization Methodologies","description":"Systematic approaches to measuring and optimizing end-to-end latency, throughput, and resource utilization in complex production environments with multiple dependencies."}
- {"name":"Hardware-Software Co-optimization","description":"Holistic understanding of how to optimize algorithms and implementations based on underlying hardware capabilities, memory hierarchies, and computational constraints."}

### databases

- {"name":"Distributed Data Storage Systems","description":"Experience working with large-scale data storage solutions for experimental datasets, benchmarking results, and production workload characterization data."}

### languages

- {"name":"Python","description":"Primary language for model evaluation, experimentation, data processing, and rapid prototyping of inference optimization and benchmarking tools."}
- {"name":"C++","description":"Used for performance-critical inference optimization code, custom CUDA kernels, and low-level system integrations requiring maximum execution efficiency."}
- {"name":"Go","description":"Leveraged for building scalable infrastructure tools, system utilities, and distributed components requiring efficient concurrency and deployment."}

### frameworks

- {"name":"PyTorch","description":"Deep learning framework for model evaluation, experimentation, and understanding model behavior during development and optimization cycles."}
- {"name":"CUDA","description":"GPU computing framework essential for optimizing inference workloads, profiling GPU utilization, and developing custom acceleration kernels for model serving."}
- {"name":"TensorRT","description":"NVIDIA's inference optimization framework used to profile, benchmark, and optimize neural network models for low-latency and high-throughput deployment."}
- {"name":"vLLM or Similar Model Serving Platforms","description":"High-performance inference frameworks for deploying and optimizing LLM serving at scale with advanced batching and memory management."}

## Benefits

### benefits

- {"name":"Comprehensive Health Coverage","description":"Medical, dental, and vision insurance plans with competitive employer contributions covering yourself and family members."}
- {"name":"Equity Participation","description":"Significant stock options grant as part of compensation package, allowing you to participate in OpenAI's growth and success."}
- {"name":"Retirement Planning","description":"401(k) retirement savings plan with employer matching to support long-term financial security."}
- {"name":"Professional Development","description":"Learning budgets, conference attendance support, and access to training resources to deepen expertise in AI infrastructure and emerging technologies."}
- {"name":"Flexible Work Arrangement","description":"Hybrid work model with 3 days per week in OpenAI's San Francisco office and flexibility for remote work. Relocation assistance provided for qualified candidates."}
- {"name":"Generous Time Off","description":"Competitive paid time off, holidays, and sabbatical opportunities to maintain work-life balance and recharge."}
- {"name":"Mental Health and Wellness","description":"Mental health support services, wellness programs, and resources to support holistic employee wellbeing."}
- {"name":"Parental Leave","description":"Comprehensive parental leave policies for primary and secondary caregivers supporting family planning needs."}

## Compensation

- **max:** 280000
- **min:** 185000
- **currency:** USD
- **stockOptions:** true

## Interview process

### steps

## Full 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](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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We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this [link](https://form.asana.com/?k=bQ7w9h3iexRlicUdWRiwvg&d=57018692298241).

[OpenAI Global Applicant Privacy Policy](https://cdn.openai.com/policies/global-employee-and-contractor-privacy-policy.pdf)

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.