About the RoleWe are looking for an
MTS - Research (Kernels & Hardware RL) team member to help us build the data engine for frontier AI. You will develop high-fidelity reinforcement learning environments and evaluation stacks that teach advanced agents to write efficient GPU kernels and solve hardware optimization problems. You will own environments end to end, from selecting meaningful tasks and reference implementations to building reliable scoring and preventing reward hacking.
Responsibilities:- Build Agentic Environments: Design diverse, executable tasks for GPU and accelerator kernels, low-level systems programming, and hardware-aware optimization. Extend into FPGA, RTL, or simulation workflows where your expertise can create meaningful challenges.
- Programmatic and Agentic Verification: Build reproducible correctness tests and performance graders, benchmark against trusted implementations, and harden execution sandboxes against timing exploits, test leakage, and reward hacking.
- Close the Loop: Analyze agent trajectories and failure modes, curate high-signal tasks and data, and collaborate on post-training experiments that improve real capability across unseen workloads and hardware constraints.
- Collaborate: Work daily with the founders and research staff to shape the roadmap and push the state-of-the-art in AI reliability.
- Scale the Infrastructure: Build reliable compilation, execution, profiling, and evaluation pipelines that support repeated experiments across hardware targets.
About YouWe are looking for individuals who demonstrate a rare combination of technical depth, research intuition, and high agency.
- Technical Foundation: A Bachelor's, Master's, or PhD in CS, Electrical or Computer Engineering, or a related technical field, or demonstrated proof of work through significant open-source contributions or industry experience.
- Engineering Rigor: Strong C/C++ and CUDA or a comparable kernel language, alongside production-quality Python. You have built and profiled kernels and understand memory hierarchy, data movement, parallelism, occupancy, and numerical correctness.
- ML Fluency: Hands-on experience with LLMs and a principled understanding of how they are evaluated and optimized. You can translate a systems problem into an executable task with clear feedback.
- Empirical Mindset: Experience conducting reproducible performance experiments, inspecting profiler results, and distinguishing real improvements from measurement noise or benchmark shortcuts.
What will make you stand out- Research Taste: You have a strong intuition for identifying what matters in complex problem spaces. You can balance deep research exploration with the pragmatism needed to ship a product.
- Impact-Driven Agency: You care about outcomes, not just activity. You don't wait for a ticket; you identify gaps in the system, build the solution, and ensure it moves real-world metrics for frontier AI labs.
- Domain Expertise: Depth in Triton, CUTLASS, LLVM/MLIR, GPU architectures, vector ISAs, FPGA/HLS, RTL/HDL, EDA tools, or architecture simulators. Experience building RL environments, agent harnesses, or robust evaluation infrastructure.
- Proven Track Record: A history of shipping high-performance kernels, compiler or hardware tooling, influential systems or ML research, or high-impact open-source projects.
- Post-Training Experience: Experience fine-tuning or evaluating large-scale models to deliver "frontier performance" on open-source benchmarks.
The base salary range for this role in California is $160,000 to $400,000 per year, depending on experience, skills, and qualifications. This role will also be eligible for equity, benefits, and bonuses.
