Member of Technical Staff, Inference

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

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Salary

$200K-$400K From the posting source

401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

157% above the BLS role benchmark for software engineering aggregate.

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source

Seniority

Mid From the posting source

Schedule

Shift type

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Weekend work

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Company

Equity

Offered From the posting source

Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Member of Technical Staff, Inference San Francisco, California, United States Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware-a position that took years to build. About the Role We're looking for an inference runtime engineer to push the boundaries of what's possible in LLM and diffusion model serving. Models grow larger. Architectures shift: mixture-of-experts, multimodal, agentic. Every breakthrough demands innovations on the inference engine itself. You'll work at the core of vLLM, optimizing how models execute across diverse hardware and architectures. Your work will directly impact how the world runs AI inference. Skills and Qualifications Minimum qualifications: - Bachelor's degree or equivalent experience in computer science, engineering, or similar. - Deep understanding of transformer architectures and their variants. - Strong programming skills in Python with experience in PyTorch internals. - Experience with LLM inference systems (vLLM, TensorRT-LLM, SGLang, TGI). - Ability to read and implement model architectures and inference techniques from research papers. - Demonstrate the ability to contribute performant and maintainable code and debug in complex ML codebases. Preferred qualifications: - Deep understanding of KV-cache memory management, prefix caching, and hybrid model serving. - Familiarity with RL frameworks and algorithms for LLMs. - Experience with multimodal inference (audio/image/video/text). - Contributions to open-source ML or system infrastructure projects. Bonus points if you have: - Implemented core features in vLLM

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