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ML Systems Engineer
Summary by AIEngineer.careers
ML Systems Engineer is a ML Infrastructure Engineer role (full-time). with Periodic Labs. in MENLO PARK, US. Compensation shown: $250K–$350K. Imported listing (source: aihiringboard.com). Apply on the employer's site (aihiringboard.com).
Imported listing (source: aihiringboard.com) · Apply on aihiringboard.com
MENLO PARK · US
$250K–$350K
Full-time
SENIOR
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from aihiringboard.com
Periodic Labs is an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs in materials, energy, and beyond. As an ML Systems Engineer, you will build and optimize the agentic infrastructure powering large-scale training, inference, and reinforcement learning. You will own critical pieces of the ML systems stack to maximize performance, scalability, reliability, and productivity for both engineers and AI agents.
Responsibilities
- Build and optimize large-scale training and reinforcement learning infrastructure while ensuring its correctness
- Develop high-performance inference and serving systems
- Design distributed runtimes and scheduling systems for complex ML workloads
- Build secure and large-scale sandboxing and execution environments
- Optimize memory, GPU kernels and communication for maximum throughput and end-to-end efficiency
- Improve scalability, reliability, and efficiency across the ML systems stack
Requirements
- Strong systems programming and performance engineering skills
- Experience building high-performance ML infrastructure at scale
- Ability to own complex technical problems end-to-end
- Strong coding ability and engineering judgment, including the ability to work effectively with AI agents to design, implement, test, and debug complex systems
Periodic Labs
Menlo Park · US · 56+ employees
Periodic Labs is a technology company founded in 2025 by former researchers from OpenAI and DeepMind that focuses on automating scientific discovery. The company is building AI scientists and autonomous laboratories that conduct physical experiments to generate high-quality experimental data for material discovery and other scientific research.
