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Member of Technical Staff, Enterprise AI

Full-timeRemote1 opening$250K - $500K/yr

About this role

As a Member of Technical Staff, you will function as a forward-deployed research partner embedded directly within enterprise AI systems. You will work on live workflows, uncover real-world failure modes, and drive rapid experimental cycles to improve system performance.

Skills

Research Signal JudgmentML-Oriented Data DesignOps-to-Research TranslationEnterprise AI

Key responsibilities

  • Embed within enterprise AI workflows as a research collaborator, working alongside domain experts and client teams.
  • Surface, formalize, and prioritize system failure modes in real-world deployments.
  • Design high-signal datasets and evaluation protocols to target identified weaknesses.
  • Run tight experimental loops to validate hypotheses and quantify improvements.
  • Produce clear, decision-oriented analyses of system behavior and performance.
  • Develop and benchmark agentic workflows, focusing on robustness and scalability.
  • Build lightweight tooling to support evaluation, data curation, and rapid iteration.
  • Contribute to internal and external research artifacts, including reports and benchmarks.

Required skills & qualifications

  • Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field.
  • Strong judgment for research signal quality, including data selection and evaluation design.
  • Experience designing datasets and evaluation frameworks for ML systems.
  • Ability to translate ambiguous operational issues into structured research problems.
  • Familiarity with RL environments and/or agentic system evaluation.
  • Clear, concise communicator with a bias toward actionable insight.
  • Proven ability to execute in fast iteration cycles and high-ambiguity settings.
  • Collaborative mindset with experience working across research, product, and domain teams.

Preferred qualifications

  • Strong client-facing experience, particularly in technical or research-driven environments.
  • Experience building internal research or evaluation tooling.
  • Contributions to benchmarks, research publications, or open research initiatives.
  • Exposure to enterprise AI deployments or forward-deployed research models.
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