Research Infrastructure Engineer, Training Systems
WFA Digital Insight
The demand for skilled ML engineers has surged in recent years, with expertise in building scalable training infrastructure being a top priority. According to industry trends, the need for professionals with strong systems instincts and ML knowledge has grown significantly. Openai's cutting-edge approach to AI research and deployment stands out in the industry, and this role offers a unique opportunity to contribute to advancing frontier models. With the remote job market booming, candidates should be prepared to showcase their skills in digital collaboration, problem-solving, and API design. As the AI landscape continues to evolve, professionals with a strong foundation in ML and software engineering are in high demand, with some reports indicating a 25% increase in job postings for related roles over the past year.
Job Description
About the Role
The Research Infrastructure Engineer role at Openai is an exciting opportunity to work on the systems layer that enables the development of large-scale ML models. As part of the research team, you will be responsible for building and maintaining the infrastructure that supports the training of these models, working closely with researchers and engineers to design and implement scalable solutions. The team's work is focused on advancing the state-of-the-art in AI research, and this role plays a critical part in making that happen.The day-to-day responsibilities of this role will involve designing and implementing APIs and interfaces that make complex training workflows easier to express and harder to misuse. You will also work on improving the reliability, debuggability, and performance of the training and data pipelines, as well as debugging issues that span multiple systems and technologies. Collaboration with other teams and stakeholders is essential, as you will be working to ensure that the infrastructure you build meets the needs of the researchers and engineers using it.
What You Will Do
- Build and maintain infrastructure for large-scale model training and experimentation
- Design APIs and interfaces that make complex training workflows easier to express and harder to misuse
- Improve reliability, debuggability, and performance across training and data pipelines
- Debug issues spanning Python, PyTorch, distributed systems, GPUs, networking, and storage
- Write tests, benchmarks, and diagnostics that catch meaningful regressions
- Work with researchers and engineers to design and implement scalable solutions
- Collaborate with other teams to ensure seamless integration of the infrastructure
- Develop and maintain documentation for the infrastructure and its usage
- Participate in code reviews and contribute to the improvement of the codebase
- Stay up-to-date with the latest developments in ML and software engineering
What We Are Looking For
- Strong systems instincts and care deeply about performance, reliability, and clean abstractions
- Experience with building and maintaining large-scale ML training infrastructure
- Good taste in API and interface design, with empathy for the researchers and engineers using your tools
- Comfortable working across ML research code and production-quality infrastructure
- Experience with debugging from evidence: profiles, traces, logs, tests, and minimal reproductions
- Strong programming skills in languages such as Python and C++
- Experience with distributed systems, GPUs, and networking
- Strong understanding of software engineering principles and practices
- Experience with collaboration tools such as Git and GitHub
Nice to Have
- Experience with cloud-based infrastructure and containerization
- Knowledge of ML frameworks such as PyTorch and TensorFlow
- Experience with automation tools such as Ansible and Terraform
- Familiarity with Agile development methodologies
Benefits and Perks
- Competitive salary and equity package
- Comprehensive health, dental, and vision insurance
- Generous PTO and holiday schedule
- Remote work stipend and support for home office setup
- Access to cutting-edge technologies and tools
- Opportunities for professional growth and development
- Collaborative and dynamic work environment
- Flexible working hours and autonomy to manage your schedule
How to Stand Out
- Make sure to highlight your experience with building and maintaining large-scale ML training infrastructure in your resume and cover letter.
- Showcase your skills in API design, debugging, and collaboration by including relevant examples in your portfolio.
- Be prepared to talk about your experience with distributed systems, GPUs, and networking during the interview process.
- Demonstrate your understanding of software engineering principles and practices, and be prepared to discuss your approach to coding and testing.
- Consider learning more about Openai's specific technologies and tools before applying, such as their use of PyTorch and containerization.
- Be prepared to discuss your experience with remote collaboration and working with distributed teams.
This is a remote position listed on WFA Digital, the platform for professionals who work from anywhere. Browse more remote jobs across all categories.