Member Technical Staff- LLMs

Member of Technical Staff – Infrastructure & LLMs

Location: San Francisco, CA (Hybrid)

Compensation: $170,000 – $220,000 base + 1–3% equity

Work Authorization: U.S. work authorization required (no visa sponsorship)

Start Date: ASAP

Type: Full-time

We’re seeking a deeply curious and technically strong engineer to join a lean, high-performance team building next-generation inference infrastructure for LLMs. This is an opportunity to own the design and development of performance-critical systems from day one, working directly on problems like:

• Scaling multi-GPU inference workloads

• Designing distributed job schedulers

• Experimenting with LLM distillation and optimization frameworks

You’ll join a two-person engineering team at the earliest stage, where your impact will be foundational to both product and culture. No bureaucracy. No politics. Just ambitious, technically challenging work that matters.

• Massive Technical Ownership: Drive core infra design with zero red tape.

• Frontier Engineering: Work on distributed systems, LLM runtimes, CUDA orchestration, and novel scaling solutions.

• Foundational Equity: Earn meaningful ownership and grow into a founding-level role.

• Mission-Driven: Focused on durable infra, not short-term hype cycles.

• No Credentials Needed: We value ability and drive over resumes and degrees.

• 2+ years experience in backend or infrastructure engineering

• Deep interest or experience in distributed systems, GPU orchestration, or AI infra

• Strong technical curiosity demonstrated through side projects, OSS contributions, or community involvement

• Background at infra-focused orgs (e.g., Supabase, Dagster, Modal, Lightning AI, Mother Duck)

• Python fluency, with production experience in Docker, GPU workloads, and distributed compute systems

• Core Language: Python

• Infrastructure: Custom distributed systems for multi-GPU inference

• Deployment: Docker, CUDA, Kubernetes (or equivalent)

• Focus: Batch inference, model distillation, low‑latency pipelines

• Fast learner with ownership mindset

• Thinks from first principles, skeptical of default assumptions

• Collaborative, positive‑sum team player

• Oriented toward building, not credentialism