Senior Machine Learning Engineer, Ads ML Efficiency

🔥 14 hours ago

🌐 United States, Canada – Remote

🇺🇸 United States – Remote 🇨🇦 Canada – Remote

💵 $216.7k - $303.4k / year

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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Reddit, Inc.

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2005

💼 Consulting

📣 Marketing

📱 Media

Consulting • Marketing • Media

Reddit, Inc. is a social media platform that acts as a hub for thousands of communities, where users can engage in diverse conversations ranging from breaking news to niche interests. It enables users to post, comment, and vote on content, fostering a vibrant online community. Millions of people globally connect and share their passions on Reddit, creating a dynamic environment for authentic human interaction.

📋 Description

• 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 • 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 while optimizing latency, throughput, hardware utilization, and cost • 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 • Contribute to technical direction and mentor engineers through collaboration, code reviews, performance debugging, and knowledge sharing • Work with Reddit’s Ads ML Efficiency team to make machine learning workloads faster, more scalable, and more cost-efficient for Ads ML teams

🎯 Requirements

• 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 • Strong software engineering fundamentals, including 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 • Ability to explain tradeoffs clearly to engineers and partner teams • Experience optimizing large-scale inference or training workloads, particularly in recommendation systems, ranking, search, or retrieval (nice-to-have) • Experience with PyTorch, Dynamo, Inductor, AOTI, torch.export, or custom operators (nice-to-have) • Experience with TensorRT, ONNX Runtime, CUDA, Triton, LLVM/MLIR, or equivalent systems (nice-to-have) • Experience with hardware-aware optimization, distributed-training performance, performance benchmarks, profiling tools, regression detection, launch-readiness capabilities, or cost observability toolings for ML workloads (nice-to-have) • Specialized compiler internals or custom CUDA/Triton kernel development are valuable but not required

🏖️ 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 • Equity in the form of restricted stock units • Commission eligibility depending on the position offered • Medical, dental, and vision insurance • 401(k) program with employer match • Generous time off for vacation • Parental leave • Interview recording opt-out opportunity