Research Engineer, Infrastructure, Training Systems
Designs and optimizes distributed training systems scaling across thousands of GPUs for large AI models. Requires strong systems engineering, PyTorch/JAX expertise, and collaborative mindset to boost research productivity.
What You’ll Do
- Design, implement, and optimize distributed training systems that scale across thousands of GPUs and nodes for large-scale training workloads.
- Develop high-performance optimizations to maximize throughput and efficiency.
- Develop reusable frameworks and libraries to improve training reproducibility, reliability, and scalability for new model architectures.
- Establish standards for reliability, maintainability, and security, ensuring systems are robust under rapid iteration.
- Collaborate with researchers and engineers to build scalable infrastructure.
- Publish and share learnings through internal documentation, open-source libraries, or technical reports that advance the field of scalable AI infrastructure.
Skills and Qualifications
Minimum qualifications:
- Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar.
- Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases.
- Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.
- Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
- A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.
Preferred qualifications:
- Past experience working on distributed training for the world’s largest models to make them stable, reliable, and performant.
- Track record of improving research productivity through infrastructure design or process improvements.
- Contributions to open-source ML infrastructure such as PyTorch, XLA, Megatron-LM, or DeepSpeed.
Logistics
Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.
Benefits: Generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
Staff Software Engineer, Infrastructure Asset Systems
As a Staff Software Engineer, you will build and extend systems for tracking, governing, and reporting on infrastructure assets. This involves designing data models, workflow engines, and integrations with financial and procurement systems, ensuring compliance and auditability.
Performance Engineer, Inference Systems
Performance engineer focused on cross-layer investigations of Anthropic's inference fleet for Claude, optimizing throughput, latency, reliability, and correctness while building observability and partnering with kernel and serving teams.
Tech Lead, Deployment & Operations — Custom Infrastructure
Lead deployment and operations for OpenAI’s custom silicon and systems into data center environments. Drive hardware bring-up, validation, production deployment, and fleet reliability at scale while leading a technical team.
Staff Fiber Network Engineer
Owns end-to-end physical layer of private global dark-fiber backbone network, including route design, fiber acquisition, vendor management, acceptance testing, and lifecycle management. Requires deep OSP/fiber expertise, optical transport knowledge, and 8+ years experience building fiber programs.
Software Engineer, Systems - Claude Code
Optimizes performance and reliability of Claude Code and Bun JavaScript runtime through low-level systems programming. Requires deep expertise in C/C++/Rust, syscalls, memory management, and runtime internals with 5+ years experience.