Senior Staff Research Scientist

• Scope, lead, and execute foundational research in learning, uncertainty estimation, and automated system diagnosis to enable adaptive perception and decision-making stacks.
• Develop novel methodologies for out-of-distribution (OOD) detection, active learning with demonstrable impact on behavior and semantics, multimodal retrieval, epistemic/aleatoric uncertainty quantification, and sensor-to-perception-to-action stack calibration.
• Partner with research leads, software engineers, and product teams to integrate breakthroughs into scalable, (and possibly real-time) autonomous infrastructure.
• Author high-impact papers in premier machine learning, computer vision, and robotics venues (e.g., CVPR, ECCV, NeurIPS, ICRA, IROS), contributing code, models, and datasets where applicable.

Minimum qualifications:

• Ph.D. in Computer Science, Robotics, Electrical Engineering, Statistics, or a related field (or equivalent practical experience).
• 6 years of experience leading research agendas, developing scalable algorithms, or deploying ML models in production or applied research environments.
  
• Experience with deep learning frameworks (e.g., JAX, TensorFlow) and with distributed training/evaluation pipelines.
  
• One or more scientific publication submission(s) for conferences, journals, or public repositories.(e.g., NeurIPS, ICML, CVPR, ECCV, ICCV, ICRA, CoRL).

Preferred qualifications:

• 6 years of experience leading complex research projects and setting technical direction in autonomous systems or mobile robotics.
• Experience designing, distilling, and optimizing neural architectures for low-latency, real-time edge hardware and onboard compute constraints.
• Proficiency in modern programming languages and working with large-scale distributed training infrastructure.
• Demonstrated track record in deep learning, epistemic/aleatoric uncertainty quantification, model calibration for decision-making, and out of distribution diagnosis and generalization. .
• Strong background in self-supervised learning, multimodal foundation models, latent and embedded spaces, or foundational representations.

• Ph.D. in Computer Science, Robotics, Electrical Engineering, Statistics, or a related field (or equivalent practical experience).
• 6 years of experience leading research agendas, developing scalable algorithms, or deploying ML models in production or applied research environments.
  
• Experience with deep learning frameworks (e.g., JAX, TensorFlow) and with distributed training/evaluation pipelines.
  
• One or more scientific publication submission(s) for conferences, journals, or public repositories.(e.g., NeurIPS, ICML, CVPR, ECCV, ICCV, ICRA, CoRL).