Software Engineer, Systems ML

Design, build, and optimize large-scale ML training and inference systems, including distributed computing frameworks and hardware-accelerated pipelines

Develop and maintain high-performance ML infrastructure components in C++ and Python, ensuring reliability, scalability, and low-latency execution

Identify and resolve performance bottlenecks across the ML stack using profiling, instrumentation, and benchmarking tools

Architect and evaluate trade-offs in ML system design, including memory bandwidth, compute utilization, and I/O throughput

Partner with research and product teams to translate ML model requirements into efficient infrastructure solutions

Define and track system-level metrics and service level objectives to maintain production reliability of ML serving systems

Lead technical design reviews and contribute to engineering standards for ML systems across the organization

Mentor other engineers on ML infrastructure best practices, debugging methodologies, and performance optimization techniques

Drive adoption of AI-augmented development workflows to expand engineering productivity and broaden the scope of deliverables

Contribute to staged rollout strategies using feature flagging and experimentation frameworks to safely deploy ML system changes

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 machine learning systems, AI infrastructure, or high-performance computing

Experience developing and optimizing ML training or inference pipelines using frameworks such as PyTorch, Tensor Flow, or equivalent

Experience with distributed computing architectures and large-scale systems design for ML workloads

Experience programming in C++ and Python for performance-critical systems

Experience using profiling and performance analysis tools to identify and resolve bottlenecks in ML or compute-intensive systems

Experience optimizing large-scale ranking and recommendation model inference on AI accelerator hardware

Experience with hardware-software co-design, including numerics optimization and SIMD or vectorization techniques

Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)

Experience with GPU programming using CUDA, ROCm, or equivalent hardware accelerator kernel development

Experience with ML compiler technologies such as MLIR, LLVM, TVM, XLA, or IREE

Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)