Staff Research Engineer, Frontier AI & Evaluation (Remote)

Crossing Hurdles

United States

Remote

USD 48,000 - 103,000

Part time

40 hours ago
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Job summary

Crossing Hurdles is seeking a Staff Research Engineer for Frontier AI, RL & Evaluation on a remote, short-term contract. You will conduct research, design experiments, and develop evaluation frameworks to advance AI capabilities.

Ideal candidates have a PhD or Master’s in AI/ML/CS, strong ML foundations, and hands-on experience with Python and modern AI frameworks. Collaboration across teams to translate research into scalable solutions is essential.

Qualifications

  • PhD or Master’s degree in AI, ML, CS, or related field.
  • Strong research engineering experience in ML or frontier AI.
  • Strong foundations in ML with experimental design and training experience.
  • Experience in synthetic data generation, reinforcement learning, or AI evaluation.
  • Proficient in Python and familiar with modern AI frameworks.

Responsibilities

  • Conduct research on frontier AI systems and investigate their capabilities and limitations.
  • Formulate research questions to inform Turing s products and technical strategy.
  • Develop research-grade datasets, experiments, prototypes, and evaluation frameworks.
  • Collaborate with teams to translate research findings into scalable applications.
  • Share findings through technical reports, publications, and conferences.

Skills

Python programming
Experiment design
ML fundamentals
Frontier AI understanding

Education

PhD or Master’s in AI/ML/CS

Tools

AI frameworks (PyTorch, TensorFlow)

Job description

Crossing Hurdles is seeking a Staff Research Engineer for Frontier AI, RL & Evaluation on a remote, short-term contract. You will conduct research, design experiments, and develop evaluation frameworks to advance AI capabilities.

Ideal candidates have a PhD or Master’s in AI/ML/CS, strong ML foundations, and hands-on experience with Python and modern AI frameworks. Collaboration across teams to translate research into scalable solutions is essential.

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