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 behaviorists9 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, coat colors, and sites, and across an archive too large to easily reprocess. Continual learning, edge-case mining, and efficient deployment are critical. You will be working with messy real-world data.

What Youll Contribute

• Set the technical direction for how the lab trains, evaluates, versions, and deploys computer vision models, including deployment to constrained edge hardware and fine-tuning of open-weight models for our downstream tasks.

• Collaborate with behavioral experts to shape the design, implementation, and deployment of behavior indices used to model human diseases in a production environment. Drive the publication and release of the resulting software.

• Own architecture decisions and their consequences: what gets built to last, what is deliberately temporary, and what we are choosing to pay for later. Set and enforce the standards 9 testing, documentation, review, reproducibility 9 that keep results repeatable after their author has left.

• Lead or co-lead evaluation of new methods and technologies, and how they would integrate with what we run today. Contribute to proposals and funding for the work you want to see done.

• Mentor lab members and contribute to technical leadership across the lab. Contribute directly to the technical growth of team members via proactive cross-team collaboration.

• Represent the lab as a senior technical voice inside JAX and externally, with collaborators, users of our released tools, and the broader research community.

What Youre Good At

• Depth in machine learning and computer vision. A masters or PhD in computer science, machine learning, or a related field is preferred, with roughly ten years of experience building and shipping software that other people depend on. You can read a current paper, judge whether the method applies to our problem, implement it, and explain its performance.

• Proficiency leveraging LLM-based coding assistants (e.g., GitHub Copilot, Claude Code) to accelerate development, while maintaining rigorous standards for code quality, correctness, and maintainability.

• Production machine learning. PyTorch, training and evaluation infrastructure, model versioning, and deployment 9 including quantization and runtime optimization for edge inference 9 plus the data plumbing that carries video at volume (object storage, containers, Kubernetes, SLURM, Go, Bash).

• Working familiarity with technologies such as Python, PyTorch, ffmpeg, C++, Cython, SQLite, PostgreSQL, SLURM, Bash, as well as cloud providers and technologies like GCP, AWS. Specialized expertise in machine learning and machine learning frameworks and tools.

• Technical-debt literacy as a senior engineer practices it. You treat taking on debt as a decision with a stated reason and a known cost, and you can say how you decide which code in a research setting has to be durable.

• Judgment about prioritization. This role demands that an individual balance competing priorities across scientific, engineering, and deadline driven requirements.

• Excellent oral and written communication. You will explain complex technical trade-offs to biologists, to institutional stakeholders, and to external collaborators, and you will contribute to manuscripts. You proactively elicit feedback, are comfortable explaining your work to scientists, and encourage discussion.

• Engagement with your work and a track record of productivity. Background in biological sciences, or a real appetite to learn the domain.

Minimum Requirements

Education: Bachelors required

Experience: 10 years required/15 years preferred

To Apply

Send a CV and one paragraph that names which of the problems above interests you and points at one system you built that other people depended on 9 what it did, what it cost to maintain, and what you would build differently now.

The salary range is $121,924 - $176,790. Salary will be determined based on qualifications and relevant years of experience.