Bachelor's degree or equivalent practical experience.
8 years of experience in software development.
6 years of experience leading research agendas and deploying ML models.
Experience with deep learning frameworks like JAX or TensorFlow.
One or more scientific publication submissions for major conferences or journals.
Responsibilities
Scope, lead, and execute foundational research in learning and automated system diagnosis.
Develop methodologies for out-of-distribution detection and active learning.
Partner with research leads and software engineers to integrate breakthroughs into scalable infrastructure.
Author high-impact papers in premier machine learning and robotics venues.
Benefits
Comprehensive health and wellness programs.
Generous paid time off and parental leave.
Retirement savings plans with company matching.
Opportunities for professional development and training.
Full Job Description
info_outline X In most instances, this position requires in-person interviews as part of the hiring process.
Minimum qualifications:
Bachelor's degree or equivalent practical experience.
8 years of experience in software development.
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:
Ph.D. in Computer Science, Robotics, Electrical Engineering, Statistics, or a related field, or equivalent practical experience.
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.
Responsibilities
Scope, lead, and execute foundational research in learning, uncertainty estimation, and automated system diagnosis to enable robust, 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.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
Google is a multinational technology company that specializes in Internet-related services and products. These include online advertising technologies, search engine, cloud computing, software, and hardware. Google was founded in 1998 by Larry Page and Sergey Brin while they were Ph.D. students at Stanford University. The company has grown tremendously since then and has become one of the most valuable companies in the world. Google's mission is to organize the world's information and make it universally accessible and useful.
