Senior Machine Learning Engineer, AI Platform

What you'll do

• Own the architecture and roadmap for core machine learning compute and inference platform components.
• Design and operate distributed systems for large-scale training and low-latency inference across thousands of accelerators.
• Optimize multi-tenancy, elasticity, and cost efficiency across a shared GPU fleet.
• Build automated pipelines to move models from experimental stages to production without re-implementation.
• Establish engineering standards for reliability, observability, and performance across the platform infrastructure.
• Translate emerging workloads into first-class platform capabilities in partnership with research and product teams.
• Provide technical leadership and mentorship to engineers across the machine learning platform organization.

What we're looking for

• 7+ years of experience building and operating large-scale platform, infrastructure, or distributed systems in production.
• Expertise in distributed systems and cloud infrastructure including Kubernetes and multi-region clusters.
• Proficiency in Python and at least one systems language such as Go, C++, Rust, or Java.
• Proven track record of designing systems that other engineers build on with clear architectural tradeoffs.
• Ability to drive measurable outcomes regarding latency, throughput, utilization, and reliability across teams.
• Experience with GPU/accelerator scheduling, performance tuning, or fleet management is preferred.
• Familiarity with ML framework internals (PyTorch, FSDP, DeepSpeed) or modern inference stacks (vLLM, TensorRT-LLM, Triton, Ray Serve).
• Experience operating ML or data infrastructure at the scale of a major product organization.