About the Role
Join a small AI infrastructure team building tools to improve the performance and efficiency of model inference across diverse hardware. This hands-on role spans low-level systems engineering, production operations, and customer collaboration.
What You'll Do
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Bring new open-source models into production with support tuned for latency and throughput.
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Improve the inference serving stack, including batching, KV caching, speculative decoding, and quantization.
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Write and tune GPU kernels using CUDA, HIP, and Triton across a range of accelerator platforms.
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Design, deploy, and operate heterogeneous clusters across infrastructure vendors.
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Operate production inference systems, improving reliability, observability, and cost efficiency at scale.
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Work with customers to understand performance needs and solve technical problems.
What We're Looking For
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Typically 1 to 6 years of experience in backend, infrastructure, systems, or related engineering, with strong low-level engineering skills.
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Strong understanding of computer architecture, memory hierarchy, and operating system internals.
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Experience with CUDA, HIP, Triton, or other GPU programming, and familiarity with Python and accelerator platforms such as TPU or AWS Trainium.
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Background deploying or operating large-scale infrastructure, clusters, or GPU fleets is valuable.
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Familiarity with machine learning inference optimization techniques such as quantization, batching, KV caching, or speculative decoding is valuable.
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Strong computer science fundamentals, a record of solving demanding technical problems, and experience using AI coding tools to improve workflows.
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Comfort working directly with customers and contributing across a broad range of engineering challenges.
Compensation & Benefits
Base salary is $200,000 to $300,000 annually. Visa sponsorship is available.
Location
On-site in San Francisco, California.
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