Senior AI/ML Platform Engineer

What you'll do

• Build and operate shared infrastructure for agentic workflows including job scheduling, orchestration, and experiment tracking.
• Develop reliable systems for distributed training, inference, and large-scale agent rollout across GPU clusters.
• Create platform services for benchmark execution, correctness checking, profiling, and regression tracking.
• Maintain artifact management systems for kernels, RTL edits, logs, and formal verification data.
• Improve GPU cluster utilization through better scheduling, reliability, quota management, and workload isolation.
• Build dashboards and observability systems to monitor experiment status, resource usage, and failure modes.
• Productionize research workflows for RL systems, inference systems, and evaluation pipelines.
• Integrate the platform with compilers, ROCm/HIP tooling, profilers, simulators, and EDA tools.

What we're looking for

• Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or a related field.
• Master's degree preferred; PhD is a plus, especially with experience in ML systems, distributed systems, or GPU computing.
• Proficiency in Python and at least one systems language such as C++, Go, or Rust.
• Experience building ML platforms, AI infrastructure, distributed systems, workflow orchestration, or GPU cluster infrastructure.
• Experience with Kubernetes, Ray, Slurm, containerization, CI/CD, or large-scale compute orchestration.
• Experience with ROCm/HIP, CUDA, PyTorch, JAX, vLLM, SGLang, Triton, or similar ML frameworks and tools.
• Expertise in building experiment tracking systems, artifact stores, benchmark automation, or developer productivity tools.
• Familiarity with compiler, profiler, simulator, formal verification, EDA, firmware, or hardware performance workflows is a plus.