Senior Machine Learning Engineer, Ads ML Efficiency

What you'll do

• Own end-to-end performance optimization for high-impact production ML workloads from identification to rollout.
• Profile and diagnose bottlenecks across model execution, frameworks, runtimes, memory, and hardware utilization.
• Design and implement improvements for inference execution, batching, memory management, and compiler integration.
• Optimize ML training and serving efficiency to improve latency, throughput, and hardware utilization.
• Conduct performance benchmarking, load testing, and regression detection to support safe model launches.
• Build reusable performance capabilities including benchmarks, tooling, and engineering guidance for future deployments.
• Mentor engineers through hands-on collaboration, code reviews, and performance debugging.

What we're looking for

• Demonstrated experience diagnosing and implementing performance improvements in production or production-relevant ML workloads.
• Deep expertise in at least one ML performance area such as inference execution, ML runtimes, compiler integration, or GPU/kernel optimization.
• Strong profiling and benchmarking skills to identify bottlenecks and validate measurable end-to-end improvements.
• Proficiency in Python and systems programming technologies to implement, debug, and maintain performance-sensitive systems.
• Familiarity with production ML frameworks and performance trade-offs across model execution, runtime, memory, hardware, and infrastructure.
• Ability to balance performance improvements with numerical correctness, model quality, reliability, and maintainability.
• Experience optimizing large-scale inference or training workloads for recommendation systems, ranking, search, or retrieval (preferred).
• Experience with PyTorch, TensorRT, ONNX Runtime, CUDA, Triton, LLVM/MLIR, or equivalent performance technologies (preferred).