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Senior AI/ML Research Engineer (Computer Vision)Intuitive • Sunnyvale, California, United States
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Senior AI/ML Research Engineer (Computer Vision)

Intuitive
  • US
    Sunnyvale, California, United States
  • US
    Sunnyvale, California, United States
Jetzt Bewerben

Über

Job Description Primary Function of Position We are building advanced augmented dexterity capabilities for next-generation robotic platforms. As a Senior AI/ML Research Engineer (Computer Vision), you will develop the perception models that let our Embodied-AI system understand the surgical scene. Working within a hierarchical, multimodal stack—where a high-level model interprets sensory observations into structured intent and a low-level policy turns that intent into precise, safe, real-time control—you will focus on the vision layer: designing, training, and evaluating models that extract anatomy, instruments, actions, and surgical context from intraoperative video. You will partner with the broader AI/ML team to define how perception feeds reasoning and control, and you will drive the research-to-deployment path for your models, taking them from offline experimentation to robust, real-time performance in the OR.
Working within Intuitive's Future Forward research organization, you will identify, build and finetune the AI/ML models and algorithms that enables us to deliver safe and performant embodied AI systems. This role calls for someone who is equally comfortable getting hands-on with models and data and designing systems that scale.
Roles and Responsibilities
Develop temporal models for activity and workflow understanding: event/state recognition and fine-grained temporal action segmentation.
Benchmark in-house models against the state of the art and recommend the target perception architecture.
Define the perception input/output specification and demonstrate offline feasibility on recorded data.
Stand up a continuous-improvement loop (discrepancy flagging, active learning, human‑in‑the‑loop relabeling) and the tooling/UI needed for offline evaluation and the path to real‑time use.
Partner with annotation and data teams to shape label taxonomies, QC, and the data pipeline that feeds the AI/ML models.
Establish the path from offline evaluation on recorded data to real‑time integration, including the continuous‑improvement (human‑in‑the‑loop) data loop.
Partner with AI/ML researchers, robotics, data engineers, and other stakeholders to deliver a perception layer that enables rapid prototyping and learning while working toward a product solution.
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  • Sunnyvale, California, United States

Sprachkenntnisse

  • English
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