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. What you’ll work on: 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 What they’re looking for: Strong Python and software engineering fundamentals 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 MLComfortable working in a fast-moving environment with significant technical ownership Especially interesting backgrounds include: Distributed training and large-scale model trainingGPU 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.