Preference Model

Member of Technical Staff - Low Level & Kernels Capabilities

San Francisco · Staff+

Sponsorship not specifiedDetected 33 days ago

PythonC++Machine LearningData AnalysisLLMsFPGASystems EngineeringHardware DesignResearch

About the role

We're hiring experienced Machine Learning Engineers for our Low Level / Kernels Capabilities team.
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.

Choose which environments are worth building. A strong kernel environment hits several marks:
Scales into many diverse tasks from a single design.
Build correctness and performance scoring that's deterministic and can't be gamed: the objective is clear, and the only way to hit it is to actually write the kernel.
Kernel development experience: you write kernels and optimize them iteratively against a profiler.
Visa sponsorship & relocation support available
Have depth in an adjacent discipline; HPC/heterogeneous clusters, hardware design (RTL/HDL, HLS), compilers and kernel toolchains (MLIR/LLVM, Mojo, Triton, gem5), or formal verification (Lean, Coq, SMT).

Competitive cash and equity compensation (>90th percentile)
Opportunity to work with top machine learning engineers
Health, vision, dental, benefits
Design and build low level / kernel-focused reinforcement learning (RL) environments that target a specified model and difficulty distribution.

Preference Model is building automated ML research engineering.
Existing frontier models are brittle when applied to real-world ML tasks. The present bottleneck is the lack of high-quality RL training environments. Our first step is to build RL environments that reflect real-world complexity, with diverse tasks and robust reward functions.
Our founding team has previous experience on Anthropic's data team building data infrastructure, and datasets behind Claude. We are partnering with leading AI labs to push AI closer to achieving its transformative potential.
Existing frontier models are brittle when applied to real-world ML tasks.
The present bottleneck is the lack of high-quality RL training environments.
Our first step is to build RL environments that reflect real-world complexity, with diverse tasks and robust reward functions.
Our founding team has previous experience on Anthropic's data team building data infrastructure, and datasets behind Claude.
We are partnering with leading AI labs to push AI closer to achieving its transformative potential.

This listing is sourced directly from Preference Model's careers page and normalized into a canonical job model.