Senior Machine Learning Engineer, Ads ML Efficiency

This is a fully remote position, open to applicants in United States, +1 more country.

📋 Description

• Lead comprehensive performance optimization projects for impactful production machine learning workloads, covering everything from identifying bottlenecks to implementation, validation, and production deployment.

• Analyze and troubleshoot machine learning performance bottlenecks across model execution, frameworks, runtimes, memory, CPU/GPU utilization, and infrastructure.

• Design and execute performance enhancements involving inference execution, runtime behavior, batching, memory management, precision, compiler integration, custom operators, kernels, or other relevant techniques.

• Enhance the efficiency and scalability of machine learning training and serving while optimizing latency, throughput, hardware utilization, and costs.

• Facilitate quicker and safer model launches through performance benchmarking, load testing, regression detection, and improvements in launch readiness.

• Collaborate with model engineers and platform teams to pinpoint high-impact opportunities, integrate optimizations safely, and validate measurable end-to-end impacts.

• Develop reusable performance capabilities, including benchmarks, tooling, optimization patterns, and engineering guidance.

• Influence technical direction and mentor engineers through collaboration, code reviews, performance debugging, and knowledge sharing.

• Work alongside Reddit’s Ads ML Efficiency team to enhance the speed, scalability, and cost-effectiveness of machine learning workloads for Ads ML teams.

⛳️ Requirements

• Practical experience in ML performance engineering.

• Proven ability to identify and implement significant performance improvements in production or production-relevant ML workloads.

• In-depth knowledge in at least one area of ML performance, such as inference execution, ML runtimes, compiler integration, GPU/kernel optimization, memory systems, or training execution.

• Strong skills in profiling and benchmarking.

• Solid foundation in software engineering principles, including implementing, debugging, testing, and maintaining performance-sensitive systems.

• Proficiency in Python and related systems programming technologies.

• Familiarity with production ML frameworks and performance trade-offs related to model execution, runtime, memory, hardware, and infrastructure.

• Ability to balance performance enhancements with numerical accuracy, model quality, reliability, maintainability, and safe deployment.

• Capacity to address specific bottlenecks while considering maintainability, adoption, and future reuse.

• Ability to clearly communicate trade-offs to engineers and partner teams.

• Experience in optimizing large-scale inference or training workloads, particularly in recommendation systems, ranking, search, or retrieval (preferred).

• Familiarity with PyTorch, Dynamo, Inductor, AOTI, torch.export, or custom operators (preferred).

• Experience with TensorRT, ONNX Runtime, CUDA, Triton, LLVM/MLIR, or similar systems (preferred).

• Experience in hardware-aware optimization, distributed-training performance, performance benchmarks, profiling tools, regression detection, launch-readiness capabilities, or cost observability tools for ML workloads (preferred).

• Specialized knowledge in compiler internals or custom CUDA/Triton kernel development is advantageous but not mandatory.

🏝️ Benefits

• Comprehensive global benefit programs tailored to your lifestyle, including workspace, professional development, and caregiving support.

• Family Planning Support.

• Gender-Affirming Care.

• Mental Health & Coaching Benefits.

• Group Personal Pension Scheme with Employer match.

• Private Medical and Dental Scheme.

• Income Replacement Programs.

• Bike to Work scheme.

• Flexible Vacation & Paid Volunteer Time Off.

• Generous Paid Parental Leave.

• Equity in the form of restricted stock units.

• Commission eligibility based on the position offered.

• Medical, dental, and vision insurance.

• 401(k) program with employer match.

• Generous time off for vacation.

• Parental leave.

• Opportunity to opt-out of interview recording.