Senior Machine Learning Engineer, Ads ML Efficiency

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Reddit's Ads ML Efficiency team is focused on making machine learning workloads faster, more scalable, and more cost-efficient, enabling our Ads ML teams to develop, launch, and scale increasingly sophisticated models. This role sits at the intersection of ML workloads, execution systems, and engineering leverage. The engineer will partner closely with ranking teams, serving owners, and ML Platform to identify important bottlenecks, implement targeted improvements, and connect execution and systems improvements to measurable end-to-end outcomes.

• Own end-to-end performance optimization initiatives for high-impact production ML workloads, from bottleneck identification and implementation through validation and production rollout.
• Profile and diagnose ML performance bottlenecks across model execution, frameworks, runtimes, memory, CPU/GPU utilization, and infrastructure using rigorous benchmarking and measurement.
• Design and implement performance improvements involving inference execution, runtime behavior, batching, memory management, precision, compiler integration, custom operators, kernels, or other relevant techniques.
• Improve ML training and serving efficiency and scalability, optimizing latency, throughput, hardware utilization, and cost while maintaining model quality and production reliability.
• Support faster and safer model launches through performance benchmarking, load testing, regression detection, and launch-readiness improvements.
• Partner with model engineers and platform teams to identify high-leverage opportunities, integrate optimizations safely, and validate measurable end-to-end impact.
• Build reusable performance capabilities, including benchmarks, tooling, optimization patterns, and engineering guidance that accelerate future model development and deployment.
• Contribute to technical direction and mentor engineers through hands-on collaboration, code reviews, performance debugging, and knowledge sharing.

• Hands-on ML performance engineering experience:
  Demonstrated ability to diagnose and implement meaningful 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, GPU/kernel optimization, memory systems, or training execution.
• Strong profiling and benchmarking skills:
  Ability to identify bottlenecks systematically, establish reliable baselines, and validate measurable end-to-end improvements.
• Strong software engineering fundamentals:
  Experience implementing, debugging, testing, and maintaining performance-sensitive systems, with proficiency in Python and relevant systems programming technologies.
• ML systems understanding:
  Familiarity with production ML frameworks and the performance trade-offs across model execution, runtime, memory, hardware, and infrastructure.
• Production engineering judgment:
  Ability to balance performance improvements with numerical correctness, model quality, reliability, maintainability, and safe rollout.
• Good customer and platform instincts: can solve concrete bottlenecks while keeping maintainability, adoption, and future reuse in mind.
• Strong communication: able to explain tradeoffs clearly to engineers and partner teams.

• Experience optimizing large-scale inference or training workloads, particularly in recommendation systems, ranking, search, or retrieval.
• Experience with PyTorch, including performance optimization using technologies such as Dynamo, Inductor, AOTI, torch.export, or custom operators.
• Experience with inference runtimes, GPU execution, or performance technologies such as TensorRT, ONNX Runtime, CUDA, Triton, LLVM/MLIR, or equivalent systems.
• Experience with hardware‑aware optimization, including batching, memory management, operator fusion, mixed precision, quantization, or accelerator efficiency.
• Experience with…