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
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Design, deploy, and optimize LLM inference systems across multi-node clusters, maximizing throughput and minimizing latency for production workloads.
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Implement and benchmark concrete inference optimizations.
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Profile and analyze inference bottlenecks at the systems level—from GPU kernel execution to memory bandwidth constraints.
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Build robust evaluation harnesses and benchmarking frameworks that measure accuracy, throughput, latency, and resource utilization across various parallelism strategies.
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Collaborate with research scientists to integrate new model architectures and optimizations into production inference infrastructure.
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Investigate and apply emerging techniques from research papers and open-source projects to continuously improve inference performance.
Qualifications
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B.S., M.S., or PhD in Computer Science, Electrical Engineering, or a related field (or equivalent experience).
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Experience with large-scale ML systems, GPU computing, or high-performance inference optimization.
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Strong proficiency in Python and C++/CUDA; hands-on experience with SGLang, vLLM, PyTorch, or similar inference frameworks.
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Deep understanding of GPU architecture, memory hierarchies, and parallel computing paradigms.
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Experience deploying and optimizing LLMs in production: model serving, batching strategies, distributed inference, or quantization.
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Strong systems-level debugging and profiling skills; comfort working at multiple layers of the stack from CUDA kernels to application logic.
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Familiarity with distributed computing frameworks (Ray, multi-node training/inference) is a plus.
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Self-directed problem solver who is interested in working on ambitious optimization challenges.
Why Join Us
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Work on cutting-edge LLM inference optimization problems with real-world production impact.
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Access to substantial GPU compute resources for experimentation and benchmarking.
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Collaborate with a world-class team spanning AI research, systems engineering, and EDA.
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Shape the performance characteristics of AI systems used by leading semiconductor companies.
What we offer
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$150K/yr – $350K/yr + Offers Equity. We are open to discuss above-scale compensation with exceptional candidates on a case-by-case basis.
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Unlimited PTO and full benefits (medical, vision, dental, 401k).
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Two engineering-centric offices with free parking, private gym, and free lunch, drinks and snacks.