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Artificial Intelligence Researcher

United States, San Francisco · Job Posted May 04, 2026
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Job Description

We're looking for AI Researchers to join a small, high-impact R&D team working on applied research problems across multimodal learning, LLM reliability, and intelligent data systems. You'll work on projects that include hallucination detection algorithms, LLM gating and output verification, active learning systems, sim-to-real domain transfer, and generative approaches to data — with a direct line from research to production. This is a founding R&D hire. You'll shape the research agenda, not just execute it.

Job Responsibility

  • Design and build hallucination detection systems
  • Develop LLM gating and output verification methods that decide in real time whether a model's response should be trusted, flagged, or blocked
  • Build active learning pipelines that analyze model failure patterns and automatically recommend what data to collect next, how much, and in what distribution
  • Research sim-to-real domain transfer and generative techniques that bridge the gap between synthetic and real-world data across visual, spatial, and sensor modalities
  • Collaborate with engineering and product teams to translate research into scalable platform capabilities
  • Publish and present work at top-tier venues when it advances the company's mission and your career

Requirements

  • MS or PhD in Computer Science, Machine Learning, Robotics, or a related field
  • Strong foundations in deep learning, with depth in one or more of: LLM internals and evaluation, hallucination detection, generative models, representation learning, domain adaptation, sim-to-real transfer, active learning, or multimodal learning
  • Proficiency in Python and modern ML frameworks (PyTorch preferred)
  • Experience working with complex, real-world data — video, 3D, language, sensor, or a combination
  • Track record of research output (publications, open-source contributions, or shipped ML systems)
  • Ability to operate independently in an early-stage environment while collaborating closely with a small team

Nice to have

  • Experience with token probability analysis, attention probing, or other white-box/gray-box LLM inspection methods
  • Familiarity with RLHF, preference learning, or LLM alignment techniques
  • Experience with robotics data, egocentric video, or embodied AI
  • Familiarity with simulation environments (NVIDIA Isaac Sim, Unity, Unreal Engine)
  • Prior experience at a startup or in applied research bridging academia and production
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