Senior Machine Learning Engineer, Reddit

• 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

Desirable

• 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