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# ML Systems Engineer

- **Company:** CHIP'S GENERAL CARPENTRY, LLC
- **Location:** Santa Barbara, CA, United States
- **Salary:** $150,000.0 - $350,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Business Logic, C++ (Programming Language), Profiling, Nvidia CUDA, Software Debugging, Distributed Computing Environment, General-Purpose Computing on Graphics Processing Units, Python (Programming Language), Graphics Processing Unit (GPU), Pytorch, Large Language Models, Parallel Computation, Information Technology, Low Latency, Machine Learning Operations
- **Published:** June 11, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=43a551dbf6a35041

## About the Role

Do you have experience in Research?, * 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.

## Description

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., * 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.


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