Software Engineer, Systems ML - Compilers / Kernels

• Design and implement scalable systems-level software components that support large-scale ML training and inference workloads across distributed compute infrastructure
• Identify and resolve performance bottlenecks in ML pipelines through profiling, instrumentation, and targeted optimization of compute, memory, and I/O utilization
• Develop and maintain high-quality, well-tested software for ML runtime systems, including scheduling, resource management, and execution frameworks
• Collaborate with AI researchers and hardware teams to co-design software solutions that efficiently map deep learning model architectures onto accelerated hardware platforms
• Own the technical design of systems components, evaluating trade-offs across correctness, performance, reliability, and maintainability
• Establish and monitor service-level objectives and performance metrics for ML infrastructure systems, building dashboards and alerting to reduce mean time to mitigation during incidents
• Drive adoption of engineering best practices including automated testing, staged rollout strategies using feature flagging and A/B testing frameworks, and comprehensive documentation
• Mentor other engineers on the team by identifying growth areas, sharing technical expertise, and contributing to code and design reviews
• Partner with cross-functional stakeholders including product, data science, and operations teams to translate ML research requirements into production-ready systems
• Proactively identify opportunities to improve developer tooling, automation, and engineering efficiency across the ML systems organization

• Bachelor’s degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 6+ years of experience in software engineering with a focus on systems software, ML infrastructure, or distributed computing
• Experience designing and optimizing software systems for large-scale machine learning training or inference workloads
• Experience with performance analysis and optimization techniques including profiling, bottleneck identification, and low-level systems tuning
• Experience building reliable, production-grade distributed systems with well-defined observability, monitoring, and incident response practices
• Experience communicating technical decisions through design documents, architectural proposals, or cross-functional stakeholder alignment, * Experience with ML frameworks such as PyTorch, TensorFlow, or JAX, including framework internals, operator development, or runtime optimization
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience with compiler technologies or graph-level optimizations for deep learning, such as MLIR, LLVM, TVM, or XLA
• Experience with GPU or AI accelerator programming models such as CUDA, ROCm, or vendor-specific kernel libraries
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Experience applying responsible AI practices in systems design, including bias mitigation, model quality evaluation, or AI risk assessment

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