Postdoctoral researcher in clinical AI and digital health to join the Division of Biomedical Informatics & AI at MUSC.
Compensation
Pay Range 0.00 - 0.00 - 0.000
Responsibilities
Acquire, curate and preprocess EHR, clinical notes, wearable and mobile data, and design, train and evaluate machine learning, deep learning and language models.
Lead analyses and first-author manuscripts for peer-reviewed journals and conferences, and present findings at national meetings.
Contribute to NIH grant proposals, including preliminary data and aims development.
Work with collaborators to define clinically meaningful outcomes and validate models.
Help mentor graduate and undergraduate students in the lab.
Requirements
PhD in biomedical informatics, computer science/engineering, data science, biomedical engineering, biostatistics or a related field
Nice to have: Interest in pursuing a K award or other early-career funding
Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution.
Responsibilities
Expected to perform independently and become an SME.
Required active participation/contribution in team discussions.
Contribute in providing solutions to work related problems.
Collaborate with team members to continuously improve AI model performance and deployment strategies.
Assist in documenting technical processes and sharing knowledge to support team growth and project continuity.
Research scientist to advance multimodal foundation model architectures, pre‑training, and distillation for Apple.
Locations (2)
Seattle, Washington, United States
Sunnyvale, California, United States
Responsibilities
Investigate fundamental questions about multimodal foundation model design, training, and distillation
Develop and evaluate new model architectures, pre‑training objectives, data strategies, optimization methods, and teacher–student learning techniques
Co‑develop frontier models and efficient models for Apple silicon and on‑device intelligence
Design architectures that distill effectively and study teacher–student training
Develop distillation methods that preserve reasoning, multimodal understanding, instruction following, and other capabilities under constrained model capacity
Requirements
Master’s degree in machine learning, computer science, or a related technical field
AI Research Scientist: Multimodal Foundation Models - Architecture, Pre-Training & Distillation
Lead product strategy and roadmap for AI infrastructure stack, defining compiler, runtime, and framework integration features.
Responsibilities
Translate customer, engineering, and business requirements into detailed Product Requirements Documents (PRDs), technical specifications, and feature definitions.
Develop detailed product schedules, milestone plans, and release timelines while managing dependencies across multiple engineering organizations.
Define requirements for the software and AI model compilation toolchain.
Lead product partnerships and technical integrations with major machine learning frameworks and runtimes.
Define performance optimization roadmap for low-level operator libraries.
Requirements
Master of Science (M.Sc.) in Computer Science, Electrical Engineering, Mathematics, or Physics
Doctor of Philosophy (Ph.D.) in Computer Science, Electrical Engineering, Mathematics, or Physics
Nice to have: MBA
Nice to have: open-source AI infrastructure contributions
Lead product management for AI infrastructure stack, defining roadmap for compiler, runtime, and framework integration.
Responsibilities
Translate customer, engineering, and business requirements into detailed Product Requirements Documents (PRDs), technical specifications, and feature definitions.
Develop detailed product schedules, milestone plans, and release timelines while managing dependencies across multiple engineering organizations.
Define requirements for the software and AI model compilation toolchain.
Lead product partnerships and technical integrations with major machine learning frameworks and runtimes.
Define performance optimization roadmap for low-level operator libraries.
Requirements
Master of Science (M.Sc.) in Computer Science, Electrical Engineering, Mathematics, or Physics
Doctor of Philosophy (Ph.D.) in Computer Science, Electrical Engineering, Mathematics, or Physics
Nice to have: MBA
Nice to have: open-source AI infrastructure contributions
Lead research on post‑training methods for generative robot policies, enabling rapid adaptation and continual learning in real‑world robotic systems.
Compensation
The pay range for this position at commencement of employment is expected to be between $158,400 and $227,700 /year for Massachusetts-based roles.
Responsibilities
Design and implement methods for adapting generative robot policies using reinforcement learning, imitation learning, agentic tool use, and related methods.
Develop methods that enable generative robot policies to improve through ongoing interaction with people and the environment, using signals such as demonstrations, interventions, preference feedback, learned rewards, and other human‑driven signals.
Develop rollout‑based learning and exploration strategies that enable robots to efficiently and safely gather useful experience, leveraging multimodal data from demonstrations, teleoperation, interventions, and on‑policy rollouts, in both simulation and on real robot hardware.
Collaborate across teams in perception, control, foundation models, and ML infrastructure to deploy scalable and reproducible research systems.
Publish research outcomes and contribute to the open robotics and embodied AI communities.
Requirements
Ph.D. or M.S. in Robotics, Machine Learning, Computer Vision, or a related field, or equivalent applied research experience.
Lead the design, development and implementation of Responsible AI capabilities across the Commonwealth Bank Group, providing technical leadership and guiding architecture, risk controls, and delivery practices for Generative and Agentic AI systems.
Responsibilities
Provide technical leadership in the design, development and implementation of world‑class evaluation and assurance capabilities for Generative and Agentic AI
Partner with engineering, data science, risk and policy teams in designing and delivering Responsible AI capabilities
Partner with risk and policy teams in building and operating formal risk controls
Establish executive risk reporting for senior leadership
Apply statistical methods and structured problem solving to complex Responsible AI challenges
Requirements
Tertiary qualification in Data Science, Statistics, Computer Science, Engineering, Mathematics or a related discipline
AI Research Scientist: Multimodal Foundation Models - Architecture, Pre-Training & DistillationNewApple · Seattle, US+1 more
Research Scientist, Robot Learning - Continual AdaptationNewToyota Research Institute · Cambridge, US