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# Staff ML Performance Engineer

- **Company:** WAYVE LLC
- **Location:** Sunnyvale, CA, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Profiling, Nvidia CUDA, Distributed Systems, Python (Programming Language), Machine Learning, Software Engineering, Caching, Information Technology, Low Latency, Machine Learning Operations, Mixed Precision
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25197_959fb261669b8a829abeddd407a667d7

## About the Role

* You have 10+ years of industry experience driving performance engineering across ML systems, GPU compute infrastructure, distributed platforms or similar
 * You have optimised large-scale workloads on GPU compute clusters, for training, inference or both
 * You have written, reported and tracked performance benchmarks in an open and accessible way
 * You write high quality, well-structured and tested Python code
 * You have a BS or MS in Machine Learning, Computer Science, Engineering or a related technical discipline, or equivalent experience

Nice to have:

 * Experience with concurrent, parallel and distributed computing
 * Experience optimising inference serving systems (e.g. latency, throughput, batching, caching)
 * Experience using NVIDIA Nsight Systems or other system profilers
 * Experience implementing GPU kernels (CUDA, Triton, etc.)
 * Knowledge of computing fundamentals - what makes code fast, secure and reliable

Not ticking every box? That's totally okay! If you're passionate about autonomy and keen to learn, we encourage you to apply even if you don't meet every requirement.

## Description

You'll identify, quantify and deliver optimisations across training and cloud inference workloads. You'll profile workloads to find bottlenecks, build optimisations that work across targets rather than one-off fixes, and track the gains with clear benchmarks. You'll work closely with Research and model teams to make performance engineering part of their development cycle, and with platform teams on cloud GPU hardware strategy., * Profiling ML workloads across training and cloud inference to identify bottlenecks, using system and kernel level profilers

 * Designing and implementing efficiency improvements to maximise MFU, throughput and utilisation, e.g. parallelism, compilation, mixed precision, caching
 * Building reusable, cross-target optimisations (kernels, data loaders, frameworks such as Triton) rather than one-off, per-workflow fixes
 * Designing and implementing benchmarking tools to track efficiency gains and catch regressions on priority training and cloud inference workloads
 * Informing cloud GPU hardware strategy and readiness in partnership with platform teams
 * Building a culture of performance optimisation with Research and model teams