Member of Technical Staff - Low Level & Kernels Capabilities

About the role

We are looking for experienced Machine Learning Engineers to join our Low Level / Kernels Capabilities team. The Kernels team focuses on developing reinforcement learning (RL) environments at the lowest layers of the stack, such as GPU and accelerator kernels, vector ISAs, codec and crypto primitives, FPGA work, and more.

Responsibilities

• Design and build low level / kernel-focused reinforcement learning (RL) environments targeting a specified model and difficulty distribution.
• Choose which environments are worth building, ensuring they meet criteria like being niche or genuinely hard, exercising real hardware features, and having a recognized reference to measure against.
• Develop and implement correctness and performance scoring mechanisms that are deterministic and cannot be gamed, ensuring the objective is clear and achievable through actual kernel development.

Requirements

• Strong low-level/systems engineering skills: fluency in C/C++/CUDA (or an equivalent kernel language), comfort with assembly when necessary.
• Strong, engineering-quality Python: writing production code, automation and deployment scripts, data analysis and plotting.
• Hardware-aware coding: designing with the silicon in mind, considering memory hierarchy, occupancy, data movement, parallelism, latency vs throughput.
• Kernel development experience: iterative optimization of kernels against a profiler.
• An adversarial mindset: turning fuzzy goals into robust, ungameable scoring, and anticipating how models might cheat.
• Ownership and autonomy: building, debugging, and shipping end-to-end with minimal supervision.

Qualifications

• Experience shipping a kernel that approached state-of-the-art (SOTA) and explaining the remaining gap.
• Depth in a niche hardware target or ISA: FPGA/HLS, RISC-V Vector, DSPs, SIMD/AVX, TPUs.
• 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).
• Ability to read and apply performance and architecture papers to practical code.
• Open-source contributions relied upon.
• A strong competitive-programming background (ideally in a low-level language).
• Experience with RL environments, agent harnesses, or evaluation infrastructure.

Benefits

Competitive cash and equity compensation (>90th percentile), ownership and autonomy in a fast-moving startup environment, opportunity to work with top machine learning engineers, health, vision, dental benefits, 401K match, lunch provided daily onsite, weekly snack orders, visa sponsorship & relocation support available.