# Member of Technical Staff - Low Level & Kernels Capabilities at Preference Model Labs

- Company: Preference Model Labs
- What the company does: Preference Model is building the next generation of training data to power the future of AI. Backed by a16z and South Park Commons.
- Company website: https://www.preferencemodel.com
- Type: Startups (AI role)
- Level: Senior
- Location: San Francisco
- Work setup: On-site
- Pay: $200K to $350K base salary per year (USD)
- Posted: 2026-08-25
- Apply by: 2026-10-09
- Apply: https://jobs.ashbyhq.com/Preference-Model/85414c2b-d0e1-4c49-a9cb-a2270f214e5a
- Page: https://www.1752.vc/careers/jobs/preference-model-labs-member-of-technical-staff-low-level-and-kernels-capabiliti/

## About the role

We’re hiring experienced Machine Learning Engineers for our Low Level / Kernels Capabilities team. The Kernels team builds reinforcement learning (RL) environments at the lowest layers of the stack. Think GPU and accelerator kernels, vector ISAs, codec and crypto primitives, FPGA work, and more. These are the domains where frontier models are weakest, niche paradigms, hardware underrepresented in training data, and open benchmarks that show models lagging.

## What they're looking for

- Strong low-level/systems engineering: fluent in C / C++ / CUDA (or an equivalent kernel language), comfortable dropping to assembly when it matters
- Strong, engineering-quality Python across your prior work, writing production code, automation and deployment scripts, data analysis and plotting (not notebook-only)
- Hardware-aware coding: you write with the silicon in mind, considering memory hierarchy, occupancy, data movement, parallelism, latency vs throughput etc
- Kernel development experience: you write kernels and optimize them iteratively against a profiler
- An adversarial mindset: you turn fuzzy goals into robust, ungameable scoring, and you ask "how would a model cheat this?"
- Hands-on work with LLMs

Tags: Engineering