About this opportunity
Open Data Science lists this RLEE - Low-Level Engineering & Kernel Inference Optimization opportunity in san francisco, California. Review the employer’s description below for duties, qualifications and application requirements.
Job description
RLEE - Low-Level Engineering & Kernel Inference Optimization
RL Environments Kernel Optimization GPU/CUDA Compilers (LLVM/MLIR) PyTorch Extensions Distributed Inference (vLLM/NCCL)
Brief Description of the Role
We're hiring Low-Level Engineers to design and build RL environments that teach LLMs kernel development, hardware optimization, and systems programming. The goal is to create realistic feedback loops where models learn to write high-performance code across GPU and CPU architectures.
This is a remote contractor role with ≥4 hours overlap to PST and advanced English (C1/C2) required.
About the Company
Preference Model is building the next generation of training data to power the future of AI. Today's models are powerful but fail to reach their potential across diverse use cases because so many of the tasks that we want to use these models are out of distribution. Preference Model creates RL environments where models encounter research and engineering problems, iterate, and learn from realistic feedback loops.
Our founding team has previous experience on Anthropic's data team building data infrastructure, tokenizers, and datasets behind the Claude model. We are partnering with leading AI labs to push AI closer to achieving its transformative potential.
The company is backed by Tier 1 Silicon Valley VC.
Responsibilities
Design and build MLE/SWE environments and diverse tasks.
Target a specified language model and satisfy the required difficulty distribution.
Requirements
Minimal Qualifications
Strong Python (engineering-quality, not notebook-only)
Clear understanding of LLMs, their current limitations
Ability to meet throughput expectations and respond quickly to feedback
You may be a good fit if one of the following applies
Deep understanding of memory hierarchies (registers, L1/L2/shared memory, HBM, system RAM) and their performance implications
Threading models, synchronization primitives, and concurrent programming (warps, thread blocks, barriers, atomics)
Cache coherence, memory access patterns, coalescing, and bank conflicts
AOT compilation and optimization passes (LLVM, MLIR, TVM)
Compiler and kernel frameworks such as CUTLASS, BitBLAS, or JAX/Pallas
Modern C++, including templates, concurrency, and build systems
Assembly-level programming and low-level optimization across GPU and CPU architectures (e.g., x86, ARM, NVIDIA Hopper, NVIDIA Blackwell)
Debugging and optimizing GPU kernels using CUDA and/or HIP/ROCm
Developing PyTorch custom operators, backend extensions, or dispatcher integrations (e.g., ATen, TorchScript, or custom backends)
Customizing, extending, or optimizing vLLM, including distributed inference workflows
GPU communication libraries and collectives, such as NVIDIA NCCL, AMD RCCL, MPI, or UCX
Mixed-precision and low-precision kernels (e.g., FP16, BF16, FP8, INT8), including numerical stability and performance trade-offs
Working conditions
Hourly contractor rate: 90- 125 USD/hour (dependent on the expertise level and quality of take-home assignment).
Contacts
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Worksite address
san francisco, CA, 94199, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.