Senior Machine Learning Engineer, Ads ML Efficiency

3d•$216,700 - $303,400•Remote

About The Position

Reddit’s Ads ML Efficiency team makes machine learning workloads faster, more scalable, and more cost-efficient so Ads ML teams can develop, launch, and scale increasingly sophisticated models. This role works at the intersection of ML workloads, execution systems, and engineering leverage, partnering with ranking teams, serving owners, and ML Platform to identify bottlenecks and deliver measurable end-to-end improvements. The engineer will lead performance optimization initiatives for production ML workloads, covering diagnosis, implementation, validation, and rollout. The role also contributes reusable benchmarks, tools, optimization patterns, engineering guidance, and technical direction that help improve model development and deployment. Reddit offers a flexible-first workforce; the role can be performed remotely from anywhere in the US or Canada.

Requirements

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, including the ability to identify bottlenecks systematically, establish reliable baselines, and validate measurable end-to-end improvements.
Strong software engineering fundamentals and experience implementing, debugging, testing, and maintaining performance-sensitive systems.
Proficiency in Python and relevant systems programming technologies.
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, maintainability, and safe rollout.
Ability to solve concrete bottlenecks while considering maintainability, adoption, and future reuse.
Strong communication skills and ability to explain trade-offs clearly to engineers and partner teams.

Nice To Haves

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.

Responsibilities

Own end-to-end performance optimization initiatives for high-impact production ML workloads, from identifying bottlenecks through implementation, 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.

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
Paid volunteer time off
Paid parental leave
Equity in the form of restricted stock units
Medical, dental, and vision insurance for U.S.-based employees
401(k) program with employer match
Commission eligibility depending on the position offered