Junior Software Developer

AI Interpretability Researcher

As a member of the Globus Labs research and software development team, this position will help launch and lead day-to-day work for the newly formed effort in AI interpretability applied to scientific discovery. The role sits at the intersection of mechanistic interpretability, scientific foundation models, and open data infrastructure. It offers a rare opportunity to shape a new research direction from the ground up while building software used by thousands of researchers. The researcher will also contribute to projects that use those models to uncover new scientific insight in domains including materials science, chemistry, catalysis, x-ray science, and more.

We're looking for a highly skilled developer and emerging researcher with experience applying interpretability methods to large language models and scientific models and using these to understand and improve model performance while also extracting key scientific insights from the models. This work also supports open science initiatives backed by the NSF, NIST, DOE, and the Genesis Mission, as well as a growing set of frontier laboratory and industry collaborations. This at-will position is wholly or partially funded by contractual grant funding which is renewed under provisions set by the grantor of the contract. Employment will be contingent upon the continued receipt of these grant funds and satisfactory job performance.

Responsibilities:

• Under the direction of the Principal Investigator, coordinate the day-to-day activities of the new effort in interpretability for scientific discovery. Help track workstreams, milestones, and deliverables, and raise issues to senior staff when needed.
• Assist in developing the group's research roadmap and priorities so they align with broader laboratory goals.
• Serve as a primary hands-on contributor to research and engineering across group projects, helping keep technical quality consistent.
• Develop tutorials and onboarding materials, and organize regular group check-ins and technical discussions, so new members ramp up quickly and knowledge is shared.
• Implement and apply mechanistic interpretability techniques to scientific foundation models and AI systems. These include feature discovery, sparse dictionary learning, circuit analysis, probing, and activation steering.
• Help investigate how models trained on scientific data represent physical, chemical, and materials concepts, and assist in evaluating how reliable interpretability methods are in these settings.
• Build and maintain reusable tooling, pipelines, and GPU-accelerated workflows for interpretability experiments.
• Support foundation model training, agentic oversight frameworks, and autonomous laboratory efforts as part of a broader team working on scientific automation.
• Contribute to high-priority laboratory initiatives as assigned, including Globus GPU-accelerated inference, ISAAC and other Genesis programs, Terminal-Bench Science, and new laboratory and industry collaborations.
• Contribute developing, deploying, and managing data infrastructure services, including systems for publishing large datasets in materials science, chemistry, and other domains.
• Help define and document requirements and architecture for secure, scalable, asynchronous data access services.
• Develop, test, document, and maintain software on a cloud-based platform, including REST, command-line, and socket-based interfaces. Apply GitOps principles for reproducible deployments.
• Work with other development teams to integrate cross-service features, and troubleshoot day-to-day technical problems using standard procedures.
• Under the guidance of the Principal Investigator, support external engagement, including partnership discussions, technical meetings, and relationships with program managers and collaborators.
• Help identify real-world applications for the group's technologies, and contribute ideas for new projects or exploratory efforts.
• Performs code testing on components and works to ensure that appropriate implementation standards are met.
• Investigates and resolves day-to-day technical problems using standard procedures.
• Performs other related work as needed.

Minimum Qualifications:

Education:

Minimum requirements include a college or university degree in related field.

Work Experience:

Minimum requirements include knowledge and skills developed through < 2 years of work experience in a related job discipline.

Preferred Qualifications:

Experience:

• Relevant experience in related job discipline may include: Research internships, research assistantships, or co-op positions in machine learning, AI interpretability, computational science, or software engineering. Internship experience at a national laborator