ML Platform Engineer, Distributed Training

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a ML Platform Engineer, Distributed Training based in the United States. Join a high-impact AI engineering team supporting healthcare and life sciences applications through large-scale foundation model training. As an ML Platform Engineer specializing in distributed training, you will work across GPU infrastructure and model training workloads on a Google Kubernetes Engine (GKE) platform powered by H200 and GB200 GPUs. You’ll collaborate with infrastructure engineers and research scientists to optimize training performance, diagnose complex failures, and improve multi-node execution at scale. Rather than focusing solely on cluster operations, you’ll take end-to-end ownership of the training stack, from workload scheduling and GPU networking to benchmarking and model evaluations. Your work will help scientists train models more efficiently while improving system reliability, scalability, and cost effectiveness. This role offers a hands-on engineering environment where technical depth, proactive problem-solving, and clear communication are essential.