Research Infrastructure Engineer - Post-Training

About the Team

Our team builds the core infrastructure and tools that transform a large pre-trained model into a cutting-edge, user-friendly chatbot. By accelerating research and development, our infrastructure enables rapid improvements and frequent model releases. We collaborate closely with research teams within the post-training team and across the company, creating systems for training, evaluation, data management, and model behavior that push the boundaries of what’s possible with ChatGPT. 

About the Role

We are seeking engineers to build cutting-edge infrastructure and user-friendly tools that are foundational to the post-training phase of ChatGPT. You will work across the entire technology stack, including working on optimizing low level ML systems, job orchestration, data and eval management, etc.

The ideal candidate possesses a strong technical background in areas such as data technologies, distributed systems, and reliable software development, with deep expertise in either ML system optimization, distributed systems, or full-stack application development for internal tools. While research experience is not mandatory, experience collaborating with ML researchers in an applied setting is highly valued. This role requires a keen ability to analyze and troubleshoot complex system issues, implement effective solutions, and proactively identify ways to prevent future failures.

This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

In this role, you will:

• Ensure that systems which power ChatGPT training and development run smoothly.

• Dive into large ML codebases to understand and debug systems issues.

• Work with researchers to build tools for data management, model configuration, evaluation, and more.

• Create reusable Python libraries with great abstractions usable across ML projects.

• Sample projects include:

  • Profiling large model reinforcement learning training and identifying and addressing bottlenecks.

  • Identifying experiment failures in a new research cluster.

  • Redesigning our data pipelines to handle diverse multimodal data.

  • Build front-end evaluation tooling for use across the company.

You might thrive in this role if you:

• Are a team player – willing to do a variety of tasks that move the team forward.

• Experience working in complex technical environments

• Experience debugging ML systems.

• Experience with reinforcement learning and or transformers

• Experience with python

• Experience with kubernetes / distributed infrastructure

• Experience with GPU’s

• Experience with 1 or more large scale data systems such as beam or spark.

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 do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or any other legally protected status. 

OpenAI Affirmative Action and Equal Employment Opportunity Policy Statement

For US Based Candidates: Pursuant to the San Francisco Fair Chance Ordinance, we will consider qualified applicants with arrest and conviction records.

We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.

OpenAI Global Applicant Privacy Policy

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.