What's the salary range?

ChipAgents lists $150,000–$350,000 for this role.

What's the tech stack?

Joblaze extracted these technologies from the posting: C++, CUDA, GPU, ML systems, PyTorch, Python.

What seniority level is this role?

ChipAgents targets mid-level candidates for this position.

Is this full-time or contract?

Full-time for this ML Systems Engineer role at ChipAgents.

ML Systems Engineer at ChipAgents

Joblaze summary

The ML Systems Engineer at ChipAgents focuses on enhancing the performance of large language model inference systems, ensuring optimal throughput and minimal latency for AI applications in chip design. Key skills include expertise in GPU computing, proficiency in Python and C++/CUDA, and experience with high-performance inference optimization. This role is ideal for candidates with a strong technical background in computer science or electrical engineering, particularly those who thrive on solving complex optimization challenges. ChipAgents, a Series A company, offers a collaborative environment with access to substantial GPU resources.

Joblaze insights

Listed 9 days ago on Joblaze — tracked directly from ChipAgents's career page.
ChipAgents has 11 other open roles including Product Line Director, AMS, Product Line Director, Frontend RCA & Coverage, Research Scientist.
6527 active Python roles on Joblaze right now.
1661 active C++ roles on Joblaze right now.
481 active PyTorch roles on Joblaze right now.
Salary band is below the typical range for AI/ML roles (median ~$188,000).
Pays more than 42% of the 3228 salaried Python roles we track (median $160,000). See Python salary trends

Quick facts

What's the salary range?: ChipAgents lists $150,000–$350,000 for this role.
What's the tech stack?: Joblaze extracted these technologies from the posting: C++, CUDA, GPU, ML systems, PyTorch, Python.
What seniority level is this role?: ChipAgents targets mid-level candidates for this position.
Is this full-time or contract?: Full-time for this ML Systems Engineer role at ChipAgents.

From the original posting

About ChipAgents

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.

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 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.