MTS - Research (Kernels & Hardware RL)

Collinear AI•Sunnyvale, CA

About The Position

Collinear is seeking an MTS - Research team member to build the data engine for frontier AI. The role focuses on developing high-fidelity reinforcement learning environments and evaluation stacks that teach advanced agents to write efficient GPU kernels and solve hardware optimization problems. The position owns environments end to end, from selecting meaningful tasks and reference implementations to building reliable scoring and preventing reward hacking. The role works closely with founders and research staff to shape the roadmap and advance AI reliability.

Requirements

A Bachelor’s, Master’s, or PhD in Computer Science, Electrical or Computer Engineering, or a related technical field, or demonstrated proof of work through significant open-source contributions or industry experience.
Strong C/C++ and CUDA or a comparable kernel language, along with production-quality Python.
Experience building and profiling kernels, with understanding of memory hierarchy, data movement, parallelism, occupancy, and numerical correctness.
Hands-on experience with LLMs and a principled understanding of how they are evaluated and optimized.
Ability to translate a systems problem into an executable task with clear feedback.
Experience conducting reproducible performance experiments, inspecting profiler results, and distinguishing genuine improvements from measurement noise or benchmark shortcuts.

Nice To Haves

Strong intuition for identifying what matters in complex problem spaces and balancing research exploration with practical product delivery.
A proactive, impact-driven approach to identifying gaps, building solutions, and improving real-world metrics for frontier AI labs.
Expertise 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.
A track record of shipping high-performance kernels, compiler or hardware tooling, influential systems or ML research, or high-impact open-source projects.
Experience fine-tuning or evaluating large-scale models to deliver frontier performance on open-source benchmarks.

Responsibilities

Design diverse, executable tasks for GPU and accelerator kernels, low-level systems programming, and hardware-aware optimization, with extensions into FPGA, RTL, or simulation workflows where appropriate.
Build reproducible correctness tests and performance graders, benchmark against trusted implementations, and harden execution sandboxes against timing exploits, test leakage, and reward hacking.
Analyze agent trajectories and failure modes, curate high-signal tasks and data, and collaborate on post-training experiments to improve capability across unseen workloads and hardware constraints.
Work with the founders and research staff to shape the roadmap and advance AI reliability.
Build reliable compilation, execution, profiling, and evaluation pipelines that support repeated experiments across hardware targets.