Research Software Engineer I - Center for AI and Research Computing (AIRC)

About the role

The Center for AI and Research Computing (AIRC) at the Salk Institute seeks a Research Software Engineer I to join our team. This role is part of a dynamic environment where rapid research priorities and innovative technical solutions are essential.

Responsibilities

• Design, build, and deploy internal software applications, web interfaces, and platforms that enable scientific research and the adoption of artificial intelligence across the Institute.
• Stand up and maintain self-hosted systems and services, including data-sharing platforms, web frontends, and collaboration tools, on centralized on-premise and cloud infrastructure.
• Support the administration, configuration, and integration of high-performance computing (HPC) resources, including GPU cluster and job scheduler (e.g., SLURM) transitions and workflows.
• Aid in the provisioning, configuration, and rollout of enterprise artificial intelligence tools and services (e.g., large language model platforms and APIs), and support their adoption by researchers and staff.
• Rapidly prototype, build, and iterate on small internal applications and utilities in response to evolving scientific and operational needs.
• Write well-documented, tested code and follow professional software engineering practices, including version control (Git), code review, and continuous integration/continuous deployment (CI/CD).
• Integrate systems and services through APIs, authentication and single sign-on, and automation to create seamless workflows for end users.
• Collaborate with the Information Technology department and other units on infrastructure, deployment, and security.
• Work directly with scientists to understand their needs and translate them into technical solutions that advance scientific AI enablement.
• Prepare and maintain documentation and training materials to enable researchers and staff to use the tools and systems developed.
• Participate in project planning and status discussions, and coordinate with other Salk labs, Core facilities, and programs.
• May assist with the onboarding or supervision of student trainees and interns.

Requirements

• BS degree in computer science, engineering, quantitative science, or a related discipline is preferred.
• 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, and experience building complete applications spanning back-end logic, front-end interfaces, and data storage.
• 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 infrastructure through code, scripting, and automation.
• Comfort working in Linux/Unix environments with version control (Git), testing, and code review.

Qualifications

• Master's degree or post-baccalaureate certification in a computational or scientific field.
• Prior internship, research, or work experience in a scientific, laboratory, or academic environment.
• Exposure to, or eagerness to quickly learn, high-performance computing environments and job schedulers such as SLURM; prior HPC operations or systems-administration experience is welcome but not required.
• Experience deploying and maintaining self-hosted applications and services, including containerization (e.g., Docker, Kubernetes) and cloud platforms (e.g., AWS, GCP).
• Full-stack web development experience with modern frameworks (e.g., React, Svelte, Vue, FastAPI, Django) and database design.
• Experience building on or integrating large language model and AI tools and APIs (e.g., the Claude API, agentic coding tools), including provisioning and enabling AI tools for non-technical users.
• Academic coursework, research experience, or demonstrated interest in biology, neuroscience, or another life-science domain.
• A track record of shipping small tools or projects quickly and independently, and of picking up new languages, frameworks, and systems with minimal ramp-up.
• Strong communication skills and the ability to work directly with scientists to translate their needs into technical solutions.
• A public code portfolio (e.g., GitHub) that demonstrates initiative beyond coursework, such as personal projects or contributions to open-source software.