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.

Design and build low level / kernel-focused reinforcement learning (RL) environments that target a specified model and difficulty distribution.

Choose which environments are worth building. A strong kernel environment hits several marks:

Targets a niche or genuinely hard domain;

Exercises real hardware features (tiling, streaming, async copy, vector ISAs);

Interesting hardware or simulators (FPGAs, novel accelerators, gem5);

Research-motivated, grounded in benchmarks where models lag;

Has a recognized reference to measure against (cuBLAS/FFTW/OpenSSL/etc.);

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.

What We are Looking For (Qualifications):

Member of Technical Staff - Low Level & Kernels Capabilities

Responsibilities

Qualification

Required

Benefits

Company

Preference Model

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