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RLEE - Low-Level Engineering & Kernel Inference Optimization

$90-125/hr•ContractRemote

location_on24;26, Caselli Avenue, Castro District, San Francisco, California, 94114, United States

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Job Description

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.

About 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.

Work location

Work model: Remote

24;26, Caselli Avenue, Castro District, San Francisco, California, 94114, United States

San Francisco, California

Open in Google Maps(37.75988, -122.44006)

Key Responsibilities

  • check_circleDesign and build MLE/SWE environments and diverse tasks
  • check_circleSWE environments and diverse tasks
  • check_circleTarget a specified language model and satisfy the required difficulty distribution

Requirements

  • verifiedStrong Python (engineering-quality, not notebook-only)
  • verifiedClear understanding of LLMs and limitations
  • verifiedAbility to meet throughput expectations
  • verified≥4 hours overlap to PST
  • verifiedAdvanced English (C1/C2)
  • verifiedRemote contractor role
  • verifiedDeep understanding of memory hierarchies
  • verifiedThreading models and concurrent programming
  • verifiedCache coherence and memory access patterns
  • verifiedAOT compilation and optimization passes
  • verifiedCompiler and kernel frameworks
  • verifiedModern C++
  • verifiedAssembly-level programming
  • verifiedDebugging and optimizing GPU kernels
  • verifiedDeveloping PyTorch custom operators

Frequently asked questions about RLEE - Low-Level Engineering & Kernel Inference Optimization at Open Data Science

What does a RLEE - Low-Level Engineering & Kernel Inference Optimization at Open Data Science do?expand_more
A RLEE - Low-Level Engineering & Kernel Inference Optimization at Open Data Science is responsible for the following: Design and build MLE/SWE environments and diverse tasks; SWE environments and diverse tasks; and Target a specified language model and satisfy the required difficulty distribution.
What are the requirements for this RLEE - Low-Level Engineering & Kernel Inference Optimization role?expand_more
To qualify for the RLEE - Low-Level Engineering & Kernel Inference Optimization at Open Data Science position, applicants should have: Strong Python (engineering-quality, not notebook-only); Clear understanding of LLMs and limitations; Ability to meet throughput expectations; ≥4 hours overlap to PST; Advanced English (C1/C2); and Remote contractor role.
What is the salary range for RLEE - Low-Level Engineering & Kernel Inference Optimization at Open Data Science?expand_more
The advertised salary range for RLEE - Low-Level Engineering & Kernel Inference Optimization at Open Data Science is $90-125/hr.
Where is the RLEE - Low-Level Engineering & Kernel Inference Optimization role at Open Data Science located?expand_more
RLEE - Low-Level Engineering & Kernel Inference Optimization at Open Data Science is based in 24;26, Caselli Avenue, Castro District, San Francisco, California, 94114, United States. This is a remote role.
Is this RLEE - Low-Level Engineering & Kernel Inference Optimization job remote, hybrid, or on-site?expand_more
Open Data Science has listed this RLEE - Low-Level Engineering & Kernel Inference Optimization role as remote.
How much experience is required for this RLEE - Low-Level Engineering & Kernel Inference Optimization role?expand_more
Candidates for RLEE - Low-Level Engineering & Kernel Inference Optimization at Open Data Science should have mid level.
What skills do you need for the RLEE - Low-Level Engineering & Kernel Inference Optimization role at Open Data Science?expand_more
Key skills for RLEE - Low-Level Engineering & Kernel Inference Optimization at Open Data Science include python; llm; gpu; cuda; llvm; mlir; pytorch; and vllm.
What category does the RLEE - Low-Level Engineering & Kernel Inference Optimization role belong to?expand_more
RLEE - Low-Level Engineering & Kernel Inference Optimization at Open Data Science is part of the Information Technology job category on Recrutus.