ML Systems, LLM Post-Training
ChipAgents, San Jose
About the job
Position overview
We are seeking an ML Systems Engineer to optimize the performance and efficiency of large language model inference powering our agentic AI platform. This is a technical role focused on low-level systems optimization. You will implement performance optimizations, build evaluation harnesses, and architect multi-node clusters for training and inference that push the limits of LLM throughput and latency. Your work will directly impact the responsiveness and cost-efficiency of AI agents used by leading semiconductor companies to design chips.
Key responsibilities
- Design, deploy, and optimize LLM inference systems across multi-node clusters, maximizing throughput and minimizing latency for production workloads.
- Implement and benchmark concrete inference optimizations.
- Profile and analyze inference bottlenecks at the systems level—from GPU kernel execution to memory bandwidth constraints.
- Build robust evaluation harnesses and benchmarking frameworks that measure accuracy, throughput, latency, and resource utilization across various parallelism strategies.
- Collaborate with research scientists to integrate new model architectures and optimizations into production inference infrastructure.