**Job Description** The Division of Metabolism, Endocrinology and Nutrition Administration has an outstanding opportunity for a Research Scientist/Engineer 3 to support the Bowen laboratory. Field of research this position is engaged in: The Bowen lab leverages wide-scale neural recordings, predictive modeling, and continuous glucose monitoring with the goal of building foundational integrated (“multi-modal”) neural + behavioral disease-state models. **The purpose of the research project(s) this position supports:** The purpose of the research supported by this position is to develop a computational pipeline for transforming large-scale mouse behavior video data into standardized (canonical) representations that can be integrated with neural and physiological measurements. This project builds on existing methods, including a foundational model for mapping 2D mouse images to a canonical 3D mesh, with the goal of extending to full 3D reconstruction. This will enable inferring canonical 3D postures from single-view video data, which will provide a unifying modality for combining neural and physiological data across various experimental conditions. Research Sponsors/Stakeholders: HHMI Hanna Gray Fellowship **Position’s major duties and responsibilities** Method Development: 3D Posture and Canonical Representation (40%) Develop and validate canonical 3D posture estimation frameworks using core multi-view, ground-truth datasets, including methods for calibration, triangulation, and uncertainty quantification. Design and implement a robust translation layer that generalizes these 3D posture models to large-scale 2D video datasets, enabling reliable inference across diverse recording conditions, camera configurations, and experimental paradigms. Evaluate model performance across datasets and conditions, and iteratively refine algorithms to ensure scalability, reproducibility, and biological interpretability. Behavioral Representation Learning and Temporal Modeling (30%) Build canonical behavioral embeddings that capture posture dynamics and action structure using modern representational learning approaches. Apply temporal modeling techniques to learn multiscale behavioral sequences and transitions. Establish a shared latent behavioral space that enables alignment and joint analysis of behavior with neural recordings and physiological signals. Collaborate with domain experts to ensure learned representations are interpretable, biologically meaningful, and suitable for downstream scientific discovery. Pipeline Architecture, Integration, and Scalability (20%) Contribute to the building and maintenance of an end-to-end, production-grade pipeline encompassing scalable video preprocessing, model training, and inference workflows. Implement GPU-accelerated training and inference, standardized evaluation protocols, and performance monitoring. Manage versioned datasets and models, ensuring reproducibility, traceability, and compatibility with evolving methods. Produce clear documentation, APIs, and interfaces to support downstream analysis modules and facilitate reuse by collaborators. Collaboration, Coordination, and Technical Leadership (10%) Serve as a primary technical point of contact for internal and external collaborators. Advise collaborators on data formatting standards, behavioral annotation and sequencing, and integration pathways into the canonical pipeline. Coordinate closely with lab members working on neural and physiological data integration, ensuring alignment between behavioral representations and experimental constraints. Contribute to strategic planning by identifying technical risks, dependencies, and opportunities for methodological innovation. **Minimum requirements:** Bachelor’s degree in Computer Science, Electrical Engineering, Applied Mathematics, Statistics, or a related field and four years of relevant experience in Machine Learning/Computer Vision/software engineering. Equivalent education and/or experience may substitute for minimum qualifications except when there are legal requirements, such as a license, certification, and/or registration. **Specific knowledge, skills and abilities required to perform the job satisfactorily include:** Demonstrated experience developing and/or implementing machine learning models in Python with PyTorch (or equivalent deep learning framework). Experience with computer vision workflows (e.g., pose estimation, segmentation, tracking, multi-view geometry, 3D vision). Strong software engineering fundamentals: version control, code review, testing, documentation, and reproducible experiments. **Specific knowledge, skills and abilities that are desirable, providing for an enhanced level of job performance, include:** Ability to communicate technical concepts clearly and collaborate in an interdisciplinary research environment. Experience with 3D reconstruction pipelines and/or mesh-based representations. Experience scaling model training/inference on GPUs; familiarity with CUDA-adjacent performance considerations, distributed training, or workflow orchestration. **Compensation, Benefits and Position Details** **Pay Range Minimum:** $80,400.00 annual **Pay Range Maximum:** $97,200.00 annual **Other Compensation:** - **Benefits:** For information about benefits for this position, visit https://www.washington.edu/jobs/benefits-for-uw-staff/ **Shift:** First Shift (United States of America) **Temporary or Regular?** This is a regular position **FTE (Full-Time Equivalent):** 100.00% **Union/Bargaining Unit:** UAW Research **About the UW** Working at the University of Washington provides a unique opportunity to change lives – on our campuses, in our state and around the world. UW employees bring their boundless energy, creative problem-solving skills and dedication to building stronger minds and a healthier world. In return, they enjoy outstanding benefits, opportunities for professional growth and the chance to work in an environment known for its diversity, intellectual excitement, artistic pursuits and natural beauty. **Our Commitment** The University of Washington is committed to fostering an inclusive, respectful and welcoming community for all. As an equal opportunity employer, the University considers applicants for employment without regard to race, color, creed, religion, national origin, citizenship, sex, pregnancy, age, marital status, sexual orientation, gender identity or expression, genetic information, disability, or veteran status consistent with UW Executive Order No. 81 (https://policy.uw.edu/directory/po/executive-orders/eo-81-prohibiting-discrimination-harassment-and-sexual-misconduct/) . To request disability accommodation in the application process, contact the Disability Services Office at 206-543-6450 or dso@uw.edu . Applicants considered for this position will be required to disclose if they are the subject of any substantiated findings or current investigations related to sexual misconduct at their current employment and past employment. Disclosure is required under Washington state law (https://app.leg.wa.gov/RCW/default.aspx?cite=28B.112.080) . University of Washington is an affirmative action and equal opportunity employer. All qualified applicants will receive consideration for employment without regard to, among other things, race, religion, color, national origin, sexual orientation, gender identity, sex, age, protected veteran or disabled status, or genetic information.