Senior ML Systems Engineer

Overview

In this hands-on role, you will design and refine models that simulate compute, memory, interconnect, and communication behavior for large-scale ML systems. You’ll build tools to simulate workloads across distributed accelerator clusters and analyze end-to-end performance at scale. You’ll collaborate with hardware, software, networking, and ML teams to translate findings into design recommendations, shaping scalable ML infrastructure. This is a research-driven, technically deep position with real impact on architecture decisions at scale.

Responsibilities

• Build simulation models for compute, memory, interconnect, and communication behavior in large-scale ML systems
• Develop tools to simulate training and inference workloads across distributed accelerator clusters
• Model distributed execution patterns including collectives, synchronization, and communication bottlenecks
• Run experiments and benchmarks on real ML systems to calibrate and validate models
• Analyze end-to-end performance metrics: throughput, latency, scaling efficiency, and cost/performance tradeoffs
• Collaborate with hardware, software, networking, and ML teams to communicate findings through design recommendations

Key requirements

• Master’s or PhD in CS, Electrical or Computer Engineering, or related field
• Strong background in ML systems, distributed systems, performance engineering, or simulation
• Experience analyzing compute, communication, and memory behavior in large-scale ML systems
• Hands-on benchmarking, profiling, and measurement of ML systems
• Familiarity with distributed training concepts: data/tensor/pipeline parallelism, collectives, synchronization
• Proficiency in Python, C++, or Rust

• cross-functional collaboration
• strong communication of complex findings
• problem-solving and analytical thinking
• ML systems
• distributed systems
• performance engineering

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