ML Systems, LLM Post-Training

ChipAgents; San Jose (CA); On-site; USD 150,000 - 350,000; Full time

Benefits offered by this job

Equity

Unlimited PTO

Full benefits

On-site gym

Job summary

ChipAgents seeks an ML Systems Engineer to optimize large language model inference across multi-node clusters and to push throughput and latency bounds while reducing cost. The role is hands-on, focusing on low-level systems optimization, performance tuning, and building evaluation harnesses for production workloads.

You will work with Python and C++/CUDA, profile GPU kernels, implement inference optimizations, and collaborate with researchers to deploy new model architectures.

Qualifications

• Experience with large-scale ML systems and high-performance inference.
• Strong debugging and profiling skills across CUDA kernels to app logic.
• Production experience: model serving, batching, distributed inference, or quantization.

Responsibilities

• Design, deploy, and optimize LLM inference across multi-node clusters.
• Implement and benchmark concrete inference optimizations.
• Profile inference bottlenecks at the systems level (GPU to memory).
• Build evaluation frameworks measuring throughput, latency, and resource use.
• Collaborate with researchers to integrate new model architectures.
• Apply emerging techniques from papers/open-source projects to improve performance.

Skills

Python

C++/CUDA

GPU computing

Inference frameworks

Education

Bachelor's / Master's / PhD in CS or EE

Tools

SGLang

vLLM

PyTorch

Ray

CUDA

Job description

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.

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

Qualifications

• B.S., M.S., or PhD in Computer Science, Electrical Engineering, or a 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.

• 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.

Why Join Us

• Work on cutting-edge LLM inference optimization problems with real-world production impact.

• Access to substantial GPU compute resources for experimentation and benchmarking.

• Collaborate with a world-class team spanning AI research, systems engineering, and EDA.

• Shape the performance characteristics of AI systems used by leading semiconductor companies.

What we offer

• $150K/yr - $350K/yr + Offers Equity. We are open to discuss above-scale compensation with exceptional candidates on a case-by-case basis.

• Unlimited PTO and full benefits (medical, vision, dental, 401k).

• Two engineering-centric offices with free parking, private gym, and free lunch, drinks and snacks.