Senior Machine Learning Engineer (Ads ML Efficiency)

Location: Remote from anywhere in the United States or Canada

Salary: $216,700–$303,400 USD base salary; equity and, depending on the position offered, commission may also apply.

Company

Reddit is a community platform with more than 100,000 active communities and approximately 130 million daily active unique visitors.

What you will do

• Own end-to-end performance optimization initiatives for high-impact production ML workloads, from bottleneck identification through implementation, validation, and production rollout.
• Profile and diagnose bottlenecks across model execution, frameworks, runtimes, memory, CPU/GPU utilization, and infrastructure using rigorous benchmarking and measurement.
• Design and implement improvements involving inference execution, runtime behavior, batching, memory management, precision, compiler integration, custom operators, and kernels.
• Improve training and serving efficiency and scalability by optimizing latency, throughput, hardware utilization, cost, model quality, and production reliability.
• Enable safer and faster model launches through benchmarking, load testing, regression detection, and launch-readiness improvements.
• Partner with ranking, serving, model engineering, and ML platform teams; build reusable performance tooling and mentor engineers through collaboration, code reviews, debugging, and knowledge sharing.

Requirements

• Hands-on ML performance engineering experience, including meaningful performance improvements in production or production-relevant ML workloads.
• Deep expertise in at least one area such as inference execution, ML runtimes, compiler integration, GPU/kernel optimization, memory systems, or training execution.
• Strong profiling and benchmarking skills, including systematic bottleneck identification, reliable baselines, and validation of measurable end-to-end improvements.
• Strong software engineering fundamentals, including implementing, debugging, testing, and maintaining performance-sensitive systems; proficiency in Python and relevant systems programming technologies.
• Understanding of production ML frameworks and performance trade-offs across model execution, runtime, memory, hardware, and infrastructure.
• Production engineering judgment to balance performance with numerical correctness, model quality, reliability, maintainability, and safe rollout.
• Ability to solve concrete bottlenecks while considering maintainability, adoption, and future reuse.
• Strong communication skills for explaining technical trade-offs clearly to engineers and partner teams.

Nice to have

• Experience optimizing large-scale inference or training workloads, particularly in recommendation systems, ranking, search, or retrieval.
• PyTorch performance optimization experience, including Dynamo, Inductor, AOTI, torch.export, or custom operators.
• Experience with inference runtimes, GPU execution, or technologies such as TensorRT, ONNX Runtime, CUDA, Triton, LLVM/MLIR, or equivalent systems.
• Hardware-aware optimization experience, including batching, memory management, operator fusion, mixed precision, quantization, or accelerator efficiency.
• Distributed-training performance experience involving GPU utilization, communication, data loading, memory efficiency, or training step-time optimization.
• Experience building performance benchmarks, profiling tools, regression detection, launch-readiness capabilities, or cost observability tooling for ML workloads.
• Experience working across ML modeling, serving, and platform infrastructure teams. Specialized compiler internals or custom CUDA/Triton kernel development is valuable but not required.

Culture & Benefits

• Flexible first workforce with remote work available from anywhere in the United States or Canada.
• Global benefit programs covering workspace, professional development, caregiving, family planning, gender-affirming care, and mental health and coaching.
• Medical and dental coverage, income replacement programs, pension or 401(k) employer matching, and paid parental leave.
• Flexible vacation, paid volunteer time off, and a Bike to Work scheme.
• U.S.-based employees may receive medical, dental, and vision insurance, a 401(k) program with employer match, vacation, and parental leave.

Hiring process

• Some interviews may be recorded, transcribed, and summarized by AI; candidates can opt out before scheduled interviews.
• Personal information, including identifiers, professional and employment-related information, and audio/video recording data, may be collected to evaluate the application.