Senior Machine Learning Engineer, Ads ML Efficiency

Details

Reddit's Ads ML Efficiency team focuses on making machine learning workloads faster, more scalable, and more cost-efficient. The role sits at the intersection of ML workloads, execution systems, and engineering leverage, partnering with ranking teams, serving owners, and ML Platform to identify bottlenecks, implement improvements, and connect systems optimizations to measurable end-to-end outcomes.

Responsibilities

• 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 by 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.

Requirements

• Hands-on ML performance engineering experience, with the 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, including the ability to identify bottlenecks systematically, establish reliable baselines, and validate measurable end-to-end improvements.
• Strong software engineering fundamentals, including experience implementing, debugging, testing, and maintaining performance-sensitive systems, with proficiency in Python and relevant systems programming technologies.
• Understanding of ML systems and production ML frameworks, including performance trade-offs across model execution, runtime, memory, hardware, and infrastructure.
• Production engineering judgment and the ability to balance performance improvements with numerical correctness, model quality, reliability, maintainability, and safe rollout.
• Good customer and platform instincts, with the ability to solve concrete bottlenecks while considering maintainability, adoption, and future reuse.
• Strong communication skills and the ability to explain trade-offs clearly to engineers and partner teams.

Nice-to-Have Qualifications

• Experience optimizing large-scale inference or training workloads, particularly in recommendation systems, ranking, search, or retrieval.
• Experience with PyTorch, including performance optimization using 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 distributed-training performance, including 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 are valuable but not required. Demonstrated performance engineering depth, sound technical judgment, and measurable workload impact are valued over familiarity with any particular tool or framework.

Benefits

• Global benefit programs covering workspace, professional development, and caregiving support.
• Family planning support.
• Gender-affirming care.
• Mental health and coaching benefits.
• Group Personal Pension Scheme with employer match.
• Private medical and dental scheme.
• Income replacement programs.
• Bike to Work scheme.
• Flexible vacation and paid volunteer time off.
• Generous paid parental leave.
• U.S.-based employees may also receive medical, dental, and vision insurance, a 401(k) program with employer match, vacation time, and parental leave.

Additional Information

This job posting may span more than one career level. In addition to base salary, the position is eligible to receive equity in the form of restricted stock units and, depending on the position offered, may also be eligible to receive a commission.

The base salary range for this position is $216,700–$303,400 USD. Final offer amounts are determined by factors including skills, depth of work experience, and relevant licenses or credentials.

Reddit is a flexible-first workforce. Employees may work remotely from anywhere in the United States or Canada. In select roles and locations, interviews may be recorded, transcribed, and summarized by artificial intelligence, with an option to opt out before scheduled interviews.

Reddit is an equal opportunity employer committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans.