Senior ML Systems Engineer

We’re partnering with a well-funded, research-driven organisation at the frontier of large-scale ML infrastructure. This is a hands-on technical role for someone who enjoys going deep on performance modelling, distributed systems, and real hardware behaviour - with direct influence over architecture decisions at scale. What you’ll do Build simulation models for compute, memory, interconnect, and communication behaviour across large-scale ML systems Develop tools to simulate training and inference workloads across distributed accelerator clusters Model distributed execution patterns including collectives, synchronisation, and communication bottlenecks Run experiments and benchmarks on real ML systems to calibrate and validate simulation models Analyse end-to-end performance: throughput, latency, scaling efficiency, and cost/performance tradeoffs Collaborate with hardware, software, networking, and ML teams to communicate findings through design recommendations

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 analysing compute, communication, and memory behaviour in large-scale ML systems Hands-on benchmarking, profiling, and measurement of ML systems Familiarity with distributed training concepts: data/tensor/pipeline parallelism, collectives, synchronisation Proficiency in Python, C++, or Rust