Senior Machine Learning Engineer, Ads ML Efficiency
New
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RedditAdvertising technology
You can work remotely from anywhere in the US or CanadaFull-TimeSenior
Salary$216,700 — $303,400 USD
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Job Details
- Required Skills
- Python
Requirements
- Demonstrated ability to diagnose and implement 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 systematically identifying bottlenecks, establishing reliable baselines, and validating measurable improvements.
- 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 explain technical trade-offs clearly to engineers and partner teams.
Responsibilities
- Own performance optimization initiatives for production ML workloads, from bottleneck identification through implementation, validation, and rollout.
- Profile and diagnose bottlenecks across model execution, frameworks, runtimes, memory, CPU/GPU utilization, and infrastructure.
- Design and implement improvements to inference execution, runtime behavior, batching, memory management, precision, compiler integration, custom operators, or kernels.
- Improve training and serving efficiency by optimizing latency, throughput, hardware utilization, and cost while maintaining model quality and reliability.
- Support model launches with performance benchmarking, load testing, regression detection, and launch-readiness improvements.
- Partner with model engineers and platform teams to identify optimization opportunities and validate end-to-end impact.
- Build reusable benchmarks, tooling, optimization patterns, and engineering guidance.
- Contribute to technical direction and mentor engineers through collaboration, code reviews, performance debugging, and knowledge sharing.
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