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Member of Technical Staff – Infrastructure & LLMs (Python & DevOps)
Location: San Francisco, CA – Hybrid (2–4 days in-person)
Seniority: Open to early-career engineers, self-taught, or non-traditional backgrounds, as long as technically strong
Salary: $170K–$220K + High-equity 1–3%
Visa Sponsorship: Not available
Hiring: 1+ engineer

About the Role:
Join a two-person early-stage team building a next-generation AI-native inference platform. You’ll own foundational systems from the ground up, including distributed job schedulers, GPU scaling infrastructure, and inference pipelines that process trillions of tokens. This role offers real technical ownership and a chance to shape the product and company culture from day one.

Key Responsibilities:

Design, build, and optimize large-scale inference infrastructure

Develop distributed systems for multi-GPU batch processing

Build ultra-fast data caching, lookup pipelines, and cost/profiling tools

Collaborate closely with the founder on architecture and roadmap

Explore novel techniques for LLM distillation and inference optimization

Ideal Candidate:

Strong Python and infrastructure experience (Docker, distributed systems)

Experience with open-source contributions, personal AI projects, or startup infra roles

Product-building experience preferred; not overly specialized in DevOps/CI

Curious, resourceful, first-principles thinker

Interested in AI infra, LLMs, and scaling complex systems

Comfortable in a small, fast-moving early-stage environment

Traits to Avoid:

“I want to break into AI” types with no proven work

Job hoppers or mercenary candidates

Candidates seeking a temporary stepping stone before returning to big tech

Why Join:

Massive technical ownership – own performance-critical systems from day one

Cutting-edge problems – scale multi-GPU workloads, experiment with LLM distillation

No bureaucracy – small, autonomous team with direct impact

Meaningful equity & founder-level impact – shape the technical foundation and culture

Mission-driven – focus on foundational AI infra, not hype-driven app layers
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