ML Systems Engineer - LLM Post-Training

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.