Member of Technical Staff - Low Level & Kernels Capabilities

About Us

Preference Model is a superintelligence data research company. We build RL environments for training capable, better-aligned superintelligences.
Almost every aspect of how a model behaves is shaped by the reward signals it's trained on. The biggest problem with AIs today is that they don't always do what we intend. Sometimes that's because they aren't capable enough, and sometimes it's because they aren't aligned. Both problems come down to the training objectives.
We're trying to address both of these issues in the most direct, highest leverage way available to us, which today means researching how to build RL environments better, not just for the AIs of today but for the AIs as they will be when they outsmart us.
Over the past year, we've built RL environments for several frontier labs, and we're backed by $16M in seed funding led by a16z. Our founding team has previous experience on Anthropic’s data team building data infrastructure, and datasets behind Claude.

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.
This role blends research and engineering. It will require you to both develop novel approaches and realize them in code. You will own environments end-to-end: choose the domain, design the tasks, build the scoring and infrastructure, and harden it against reward hacking. Because the tasks run so low in the stack, robust scoring and sandboxing are a real part of the job, making sure a model can't game the timer instead of writing the kernel.

What You Will Do:

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Design and build low level / kernel-focused reinforcement learning (RL) environments that target a specified model and difficulty distribution.
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Choose which environments are worth building. A strong kernel environment hits several marks:
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Targets a niche or genuinely hard domain;
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Exercises real hardware features (tiling, streaming, async copy, vector ISAs);
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Interesting hardware or simulators (FPGAs, novel accelerators, gem5);
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Research-motivated, grounded in benchmarks where models lag;
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Has a recognized reference to measure against (cuBLAS/FFTW/OpenSSL/etc.);
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Scales into many diverse tasks from a single design.
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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.
What We are Looking For (Qualifications):
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Strong low-level/systems engineering: fluent in C / C++ / CUDA (or an equivalent kernel language), comfortable dropping to assembly when it matters.
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Strong, engineering-quality Python across your prior work, writing production code, automation and deployment scripts, data analysis and plotting (not notebook-only).
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Hardware-aware coding: you write with the silicon in mind, considering memory hierarchy, occupancy, data movement, parallelism, latency vs throughput etc.
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Kernel development experience: you write kernels and optimize them iteratively against a profiler.
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An adversarial mindset: you turn fuzzy goals into robust, ungameable scoring, and you ask "how would a model cheat this?"
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Hands-on work with LLMs
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Ownership and autonomy: you build, debug, and ship end-to-end with minimal supervision.
You may be a good fit if you also:
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Have shipped a kernel that approached SOTA and can explain the remaining gap.
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Have depth in a niche hardware target or ISA: FPGA/HLS, RISC-V Vector, DSPs, SIMD/AVX, TPUs.
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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).
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Read performance and architecture papers and turn them into running code.
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Have open-source contributions others rely on.
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Have a strong competitive-programming background (ideally in a low-level language).
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Have built RL environments, agent harnesses, or evaluation infrastructure.
What We Offer:
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Competitive cash and equity compensation (>90th percentile)
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Ownership and autonomy in a fast moving startup environment
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Opportunity to work with top machine learning engineers
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Health, vision, dental, benefits
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401K match
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Lunch provided everyday onsite
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Weekly snack orders
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Visa sponsorship & relocation support available
We value diverse perspectives and experiences. If you're excited about this role but don't check every box, we still encourage you to apply.