Senior Machine Learning Engineer, Ads ML Efficiency
New
R
Reddit, Inc.Ads machine learning
You can work remotely from anywhere in the US or CanadaFull-TimeSenior
Salary$216,700 — $303,400 USD
Apply NowOpens the employer's application page
Job Details
- Required Skills
- PythonPyTorch
Requirements
- Demonstrate hands-on ability to diagnose and implement meaningful performance improvements in production or production-relevant ML workloads.
- Bring 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.
- Use profiling and benchmarking to identify bottlenecks systematically, establish reliable baselines, and validate measurable end-to-end improvements.
- Have software engineering experience implementing, debugging, testing, and maintaining performance-sensitive systems.
- Be proficient in Python and relevant systems programming technologies.
- Understand production ML frameworks and performance trade-offs across model execution, runtime, memory, hardware, and infrastructure.
- Balance performance improvements with numerical correctness, model quality, reliability, maintainability, and safe rollout.
- Explain technical trade-offs clearly to engineers and partner teams.
- Experience with large-scale inference or training workloads, especially recommendation systems, ranking, search, or retrieval, is a plus.
- Experience with PyTorch and technologies such as Dynamo, Inductor, AOTI, torch.export, custom operators, TensorRT, ONNX Runtime, CUDA, Triton, LLVM/MLIR, or equivalent systems is a plus.
Responsibilities
- Own end-to-end performance optimization initiatives for high-impact production ML workloads, from bottleneck identification through validation and production rollout.
- Profile and diagnose bottlenecks across model execution, frameworks, runtimes, memory, CPU/GPU utilization, and infrastructure using benchmarking and measurement.
- Design and implement improvements involving inference execution, runtime behavior, batching, memory management, precision, compiler integration, custom operators, or kernels.
- Improve ML training and serving efficiency and scalability by optimizing latency, throughput, hardware utilization, and cost while maintaining model quality and reliability.
- Support model launches through performance benchmarking, load testing, regression detection, and launch-readiness improvements.
- Partner with model engineers, ranking teams, serving owners, and platform teams to identify opportunities, integrate optimizations, and validate end-to-end impact.
- Build reusable performance benchmarks, tooling, optimization patterns, and engineering guidance.
- Contribute to technical direction and mentor engineers through collaboration, code reviews, performance debugging, and knowledge sharing.
View Full Description & ApplyYou'll be redirected to the employer's site
