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Senior Machine Learning Engineer, Ads ML Efficiency
Reddit, Inc.Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in ML performance engineering, focusing on optimizing production ML workloads through performance improvements, benchmarking, and collaboration with engineering teams. Proficient in Python and familiar with various ML frameworks and performance trade-offs to ensure efficient and scalable model launches.
Highest-signal resume keywords
ML Performance EngineeringPerformance OptimizationProfiling And BenchmarkingPython ProficiencyProduction ML Frameworks
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- 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
What you’ll need- 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
Comp & perks- 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
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Performance Optimization TechniquesInference ExecutionML RuntimesCompiler IntegrationGPU/Kernel OptimizationMemory SystemsTraining ExecutionDebugging Performance-Sensitive SystemsLoad TestingRegression Detection
Soft Skills
CollaborationMentoringKnowledge SharingClear Communication
Tools & Technologies
PyTorchTensorRTONNX RuntimeCUDATritonLLVM/MLIRDynamoInductorTorch.exportCustom Operators
Industry Keywords
Machine LearningPerformance BenchmarkingModel QualityScalabilityCost EfficiencyRecommendation SystemsRankingSearchRetrieval