Senior Machine Learning Engineer, Ads ML Efficiency

What you'll do

• Profile workloads and lead end-to-end performance improvements, from bottleneck diagnosis through validation and production rollout.
• Improve ML training and serving efficiency, and build benchmarks and reusable tools to support safer, faster model launches.
• Partner with model and platform teams; contribute to technical direction and mentor engineers.

What they ask for

• Demonstrated ability to diagnose and deliver meaningful performance improvements in production or production-relevant ML workloads.
• Deep expertise in at least one ML performance area, such as inference execution, runtimes, compiler integration, GPU/kernel optimization, memory systems, or training execution.
• Strong profiling and benchmarking skills, including establishing baselines and validating measurable improvements.
• Strong software engineering fundamentals, Python proficiency, ML systems understanding, and judgment balancing performance with correctness, quality, reliability, and maintainability.

Nice to have

• Experience optimizing large-scale inference or training workloads, particularly in recommendation systems, ranking, search, or retrieval.
• Experience with PyTorch performance optimization, inference runtimes, GPU execution, hardware-aware optimization, distributed training, or ML performance tooling.

Pay

$216,700 — $303,400 USD (base salary)

Tools

Python, PyTorch, Dynamo, Inductor, AOTI, torch.export, TensorRT, ONNX Runtime

AI focus

Optimizes machine-learning training and inference workloads; the posting also states that AI may be used to record, transcribe, and summarize interviews in select roles and locations, with an option to opt out.