Machine Learning Engineer

I'm working with a rapidly growing, well-funded AI company building some of the most technically ambitious real-world AI systems today.

They're looking for an ML Infrastructure Engineer to join a highly technical team responsible for building the infrastructure that powers large-scale model training, experimentation, and deployment.

This is a hands-on engineering role for someone who enjoys solving difficult systems problems at the intersection of machine learning, distributed systems, and infrastructure.

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

• Strong Python 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
• Comfortable working in a fast-moving environment with significant technical ownership

• Distributed training and large-scale model training
• ML platforms / 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 and into production or physical-world environments

This is a great opportunity for an engineer who wants to work on hard ML systems problems at scale while being much closer to the models and real-world applications than you would be on a traditional infrastructure team.