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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