Machine Learning Engineer

Hands-on role focused on designing and scaling the infrastructure behind large-scale machine learning model training, experimentation, and deployment in San Francisco.

Responsibilities

• Build and scale infrastructure to support training of large machine learning models
• Design distributed training systems and work to improve training efficiency, reliability, and throughput
• Create and maintain data pipelines and supporting infrastructure for large-scale ML workloads
• Improve GPU utilization through compute orchestration, checkpointing, and experiment management
• Develop tooling that helps researchers and ML engineers iterate faster
• Diagnose performance bottlenecks across training, data, and compute systems
• Contribute to taking ML systems from experimentation through production and real-world deployment

Requirements

• Strong Python skills and software engineering fundamentals
• Experience building ML infrastructure, training infrastructure, or large-scale distributed systems
• Experience working with PyTorch and modern ML training stacks
• Strong understanding of distributed computing and GPU-based workloads
• Experience building reliable systems that support ML research or production ML
• Comfort working in a fast-moving environment with meaningful technical ownership

Technologies

• Python
• PyTorch

Especially Interesting Backgrounds Include

• Distributed training and large-scale model training
• ML platforms and internal training infrastructure
• GPU infrastructure and compute orchestration
• Large-scale data infrastructure and pipelines
• Performance optimization for ML workloads
• Infrastructure supporting robotics, embodied AI, computer vision, or other real-world ML systems
• Experience taking ML systems beyond research into production or physical-world environments

Location: San Francisco, CA (onsite)

Salary: USD 1,000,000 - 1,000,000 per yearly