ML Systems Engineer

ChipAgents•San Jose, CA

38d•$150,000 - $350,000•Onsite

About The Position

ChipAgents is redefining the future of chip design and verification with agentic AI workflows. Our platform leverages cutting-edge generative AI to assist engineers in RTL design, simulation, and verification, dramatically accelerating chip development. Founded by experts in AI and semiconductor engineering, we partner with top semiconductor firms, cloud providers, and innovative startups to build intelligent AI agents. The company is a Series A company backed by tier-1 VC firms. ChipAgents is deployed in production to companies that have shipped 16B chips. 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.

Requirements

B.S., M.S., or PhD in Computer Science, Electrical Engineering, or related field (or equivalent experience).
Experience with large-scale ML systems, GPU computing, or high-performance inference optimization.
Strong proficiency in Python and C++/CUDA; hands-on experience with SGLang, vLLM, PyTorch, or similar inference frameworks.
Deep understanding of GPU architecture, memory hierarchies, and parallel computing paradigms.
Experience deploying and optimizing LLMs in production: model serving, batching strategies, distributed inference, or quantization.
Strong systems-level debugging and profiling skills; comfort working at multiple layers of the stack from CUDA kernels to application logic.
Self-directed problem solver who is interested in working on ambitious optimization challenges.

Nice To Haves

Familiarity with distributed computing frameworks (Ray, multi-node training/inference) is a plus.

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.
Investigate and apply emerging techniques from research papers and open-source projects to continuously improve inference performance.