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Senior Machine Learning Engineer, Ads ML Efficiency

Reddit· Ads Product & Engineering
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Hybrid (Remote (US/Canada))$216.7k - $303.4kfull-timePosted 6d ago

About the role

Senior ML Engineer focusing on performance optimization for production ML workloads in Reddit Ads, driving faster, more scalable, and cost-efficient ML systems through end-to-end performance improvements, benchmarking, and collaboration with ranking, serving, and platform teams.

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, 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.

Qualifications

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

Skills

Required:ML performance engineeringProfiling and benchmarkingPython

Benefits

  • Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
  • Family Planning Support
  • Gender-Affirming Care
  • Mental Health & 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
  • Generous Paid Parental Leave
Last updated Oct 21, 2026. Interested? Apply on the company site.
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