Junior Software Developer

Department

Globus Labs - Materials

About the Department

Globus (www.globus.org) is a sustainable, non-profit unit within The University of Chicago delivering solutions to the research community worldwide. Globus develops and provides critical services that support scientific research for governmental, academic, and commercial organizations in a wide range of disciplines including life sciences, physics, and astronomy. We develop and operate commercial-quality, cloud-based software application and platform services used by 10s of thousands of researchers to manage their large–and growing–data management challenges. We have offices located at the NBC Tower in the heart of downtown Chicago and remote employees who work-from-home. Globus, together with Globus Labs, a research group within the University of Chicago, and part of the Data Science and Learning Division at Argonne National Labs, develop and deploy cutting edge technologies to solve new challenges facing the scientific community and enable break-through scientific discoveries.

Job Summary

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.

Certifications:

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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 laboratory, academic research group, or industry AI lab is strongly valued.
• Hands-on work on research projects involving large language models, scientific foundation models, or other deep learning systems, including undergraduate or graduate thesis work, published conference papers and preprints.
• Contributions to open-source software projects, particularly in machine learning, interpretability tooling, or scientific computing.
• Experience developing, testing, and deploying software in a collaborative team setting, whether through internships, research groups, or professional roles.
• Exposure to research outputs such as co-authored papers, workshop submissions, technical reports, posters, or conference presentations (preferred but not required).

Technical Skills or Knowledge:

• Experience developing code in Python, with knowledge of Node.js and/or a compiled language such as C/C++.
• Experience with deep learning frameworks such as PyTorch or JAX, and familiarity with model libraries such as Hugging Face Transformers.
• Familiarity with mechanistic interpretability methods, including sparse autoencoders or dictionary learning, probing, circuit analysis, activation patching, or activation steering. Experience with tools such as TransformerLens, nnsight, or SAELens is a plus.
• Understanding of transformer architectures and how large language models and scientific foundation models are trained and evaluated.
• Experience running GPU-accelerated workloads, including on HPC clusters or cloud GPU instances, and familiarity with job schedulers such as Slurm (strongly preferred).
• Knowledge of cloud computing platforms (AWS, GCP, Azure, etc.).
• Proficiency in software development best practices, including version control (Git), code review, testing, documentation, and CI/CD pipelines.
• Experience building or consuming REST APIs and command-line tools; familiarity with asynchronous or socket-based interfaces is a plus.
• Experience with data science and analysis tools (NumPy, pandas, scikit-learn, Jupyter) and with handling large datasets.
• Interest in or exposure to scientific domains such as materials science, chemistry, catalysis, or x-ray science (preferred but not required).

Preferred Competencies

• Strong analytical and problem-solving skills.
• Excellent written and verbal communication skills in English.
• Ability to work effectively in a dynamic, hybrid work environment, collaborating with other team members.
• Interest in and experience developing software to support data-driven research.

Working Conditions

• Remote work options are available for this position, with occasional attendance required at in-person meetings.

Application Documents

• Resume/CV (required)

The University of Chicago uses AI-assisted tools to streamline and augment some recruitment processes; however, AI is not used to make hiring decisions.

When applying, the document(s) MUST be uploaded via the My Experience page, in the section titled Application Documents of the application.

Job Family

Information Technology

Role Impact

Individual Contributor

Scheduled Weekly Hours

37.5

Drug Test Required

No

Health Screen Required

No

Motor Vehicle Record Inquiry Required

No

Pay Rate Type

Salary

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FLSA Status

Exempt

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Pay Range

$100,000.00 - $130,000.00

The included pay rate or range represents the University’s good faith estimate of the possible compensation offer for this role at the time of posting.

Benefits Eligible

Yes

The University of Chicago offers a wide range of benefits programs and resources for eligible employees, including health, retirement, and paid time off. Information about the benefit offerings can be found in the Benefits Guidebook.

Posting Statement

The University of Chicago is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender, gender identity, or expression, national or ethnic origin, shared ancestry, age, status as an individual with a disability, military or veteran status, genetic information, or other protected classes under the law. For additional information please see the University's Notice of Nondiscrimination.

Job seekers in need of a reasonable accommodation to complete the application process should call 773-702-5800 or submit a request via Applicant Inquiry Form.

All offers of employment are contingent upon a background check that includes a review of conviction history.A conviction does not automatically preclude University employment.Rather, the University considers conviction information on a case-by-case basis and assesses the nature of the offense, the circumstances surrounding it, the proximity in time of the conviction, and its relevance to the position.

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