Principal Scientific Software Engineer | Kumar Lab

thejacksonlaboratory; Bar Harbor (ME); In loco; USD 120.000 - 180.000; Tempo pieno

Descrizione del lavoro

The Jackson Laboratory's Kumar Lab seeks a Principal Scientific Software Engineer to shape machine learning and computer vision engineering behind digital measures. You will harden existing algorithms, set technical direction, and mentor trainees while driving publications and model releases that emerge from the work.

You will work collaboratively to translate research into robust software capable of handling neurodevelopmental and neuropsychiatric disorder studies, with emphasis on scalable

Competenze

• Independent, motivated scientist who can work collaboratively.
• Experience leading ML/CV projects and publishing results.
• Ability to mentor trainees and drive model releases.

Mansioni

• Shape ML and CV engineering for digital measures.
• Harden existing algorithms and set technical direction.
• Drive publications and model releases from the lab's work.
• Collaborate across research and software teams to deliver robust tools.

Conoscenze

Machine learning

Computer vision

MLOps

Mentoring

Publications

Descrizione del lavoro

Job Summary

The Kumar Lab studies the genetic and neurological basis of behavior with the goal of therapeutic and mechanistic discovery. We leverage machine learning and computer vision methods to model human diseases by transforming videos of mice into quantitative behavioral traits. Technology developed by our lab has been deployed in the JAX Envision System, a home cage monitoring platform. Envision streams continuous petabyte-scale video from animal housing into a cloud archive and operates on it in close to real time using multi-task algorithms. Segmentation, pose estimation, and instance assignment are all used for action recognition, action localization tasks using supervised and unsupervised approaches to quantify complex animal behaviors representative of health and disease.

As Principal Scientific Software Engineer you will collaboratively shape the machine learning and computer vision engineering behind digital measures, harden pre-existing algorithms, and leverage robust MLOps principles. You will set technical direction, mentor trainees, and drive publications and model releases that come out of the work.

A successful candidate will be independently motivated, work collaboratively, and contribute meaningfully to the development of therapeutics for neurodevelopmental and neuropsychiatric disorders.

The Challenges

Mice are inherently difficult to study because they tend to avoid detection. As highly flexible, deformable animals, they are primarily active in low-light conditions and occupy small, confined spaces. These behaviors create significant challenges for computer vision tasks such as segmentation, pose estimation, instancetrackingand identity tracking.

• Human Annotation is Expensive. Expert behaviorists' time is limited, so creating a system that enables quick, high impact, scalable annotation is a must.

• Occlusion Complicates Behavior Annotation. Group-housed mice huddle and occlude each other.

• Models Must Generalize. Measures must perform across a diversity of genetic backgrounds, environments,