ML Systems Engineer

ChipAgents • San Jose, CA

4d • 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.

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.

Nice To Haves

• Familiarity with distributed computing frameworks (Ray, multi-node training/inference) is a plus.
• Self-directed problem solver who is interested in working on ambitious optimization challenges.

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.

Benefits

• Unlimited PTO
• full benefits (medical, vision, dental, 401k)
• Offers Equity
• Access to substantial GPU compute resources for experimentation and benchmarking.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree