Research Software Engineer I - Laboratory of Dr. Talmo Pereira

Salk Institute for Biological Studies is an internationally renowned research institution dedicated to groundbreaking scientific research. The Research Software Engineer I will build and maintain software, applications, infrastructure, and analysis pipelines supporting plant root phenotyping, deep learning pose estimation, and biological discovery. The role also involves collaborating directly with biologists, contributing to open-source tools, and delivering reproducible analyses.

What you'll do:

• Develop, maintain, and release open-source scientific Python packages for pose-estimation-based phenotyping, following professional software engineering practices including version control (Git), code review, testing, type checking, and continuous integration/continuous deployment (CI/CD)
• Maintain and extend Bloom, the lab’s web application and scientific database for plant phenotyping data, spanning backend services, the API, the web frontend, schema design, and deployment on self-hosted infrastructure
• Maintain and release Bloom Desktop, the Electron/React application used on the plate and cylinder scanners to acquire images and upload them to Bloom, including its integration with camera and data-acquisition hardware
• Stand up, deploy, and maintain self-hosted systems and services on lab servers and cloud infrastructure, including database backup and restore, object storage, and TLS certificate management
• Build, operate, and improve containerized, GPU-accelerated analysis pipelines that carry raw imaging data through deep learning inference to quantitative trait outputs
• Train, evaluate, and deploy deep learning pose-estimation models, and maintain the datasets, configurations, and model registries that make training runs reproducible
• Connect analysis pipelines to Bloom through well-defined data contracts, provenance tracking, and write-back of results to the database
• Run analyses on experimental datasets for collaborating laboratories and deliver the results as documented, reproducible reports and figures
• Support and make use of shared high-performance computing resources, including GPU cluster scheduling and containerized job workflows (e.g., Kubernetes-based schedulers, SLURM)
• Rapidly prototype, build, and iterate on internal applications, command-line tools, and utilities in response to evolving scientific needs
• Integrate systems and services through APIs, authentication, and automation to create seamless workflows for end users
• Work directly with biologists and other scientists to understand their experimental goals and translate them into technical solutions

What they're looking for:

• BS degree in computer science, engineering, quantitative science, or a related discipline is preferred for this position
• This is an early-career role; no prior professional experience is required
• Demonstrated software engineering ability — through internships, research projects, open-source contributions, or personal projects — is essential
• Full-stack software engineering ability, including proficiency in Python and at least one other language (TypeScript preferred), and experience building complete applications spanning back-end logic, front-end interfaces, and data storage
• Proficiency with the scientific Python stack (e.g., NumPy, pandas, scikit-learn), including the ability to build complete, tested, documented packages rather than one-off scripts
• Experience designing and querying relational databases (e.g., PostgreSQL), including schema changes and migrations
• Working familiarity with at least one deep learning framework (e.g., PyTorch) and a conceptual understanding of how models are trained, evaluated, and deployed
• Demonstrated interest in computer vision and in open-source software development
• Fluency with modern, AI-assisted software development, including the effective use of AI coding tools and large language models (e.g., Claude Code) to design, build, debug, and ship software rapidly
• Demonstrated ability to learn unfamiliar systems and technologies quickly and to stand up working services and pipelines through code, scripting, and automation
• Comfort working in Linux/Unix environments with version control (Git), testing, and code review
• Prior internship, research, or work experience in a scientific, laboratory, or academic environment

Benefits:

• Medical
• Dental
• Vision
• Retirement
• Paid time off
• Tuition reimbursement
• Patient advocacy services
• Transit/parking program