Machine Learning Researcher

University of Chicago • Chicago, IL

1d • $110,000 - $130,000

About The Position

The Bike Shop seeks to solve society’s most pressing challenges by designing and scaling advanced algorithms—particularly AI—that enhance human capacity, not simply automate tasks. The center develops new behaviorally-informed AI technologies through foundational research, builds the tools and interventions that drive measurable social impact, and trains the next generation of scholars at the intersection of AI, behavioral science, and public policy. By translating advances into scalable, actionable solutions, the center empowers governments and institutions to achieve outcomes traditional approaches cannot. The Bike Shop is hiring a Machine Learning Researcher. The Bike Shop is a CS and Economics research lab focused on building “bicycles for the mind”, algorithms that enhance (rather than automate) human capabilities. The Machine Learning Researcher serves as a computational scientist and technical lead, supporting advanced applications of artificial intelligence and machine learning in a research lab environment. The role contributes to the technical vision and architecture for ML projects and software solutions, spanning data preparation, acquisition, ingestion, integration, model development, training, and evaluation across multiple modalities. This position engages collaboratively with faculty, PhD students, and lab researchers in computer science, policy, economics, and related disciplines to design, implement, and analyze state-of-the-art machine learning and research computing approaches. The Machine Learning Researcher represents the lab in the broader research community through publications, presentations, and technical collaborations. This position is not eligible for employer-sponsored employment authorization. This gift funded role is expected to be one year in duration but may be renewed annually.

Requirements

• Minimum requirements include a college or university degree in related field.
• Minimum requirements include knowledge and skills developed through 5-7 years of work experience in a related job discipline.

Nice To Haves

• Bachelor’s degree in computer science, engineering, mathematics, statistics, or a related technical field.
• Master’s degree or PhD in computer science, electrical engineering, data science, or a related discipline, or equivalent experience in an ML engineering or research environment.
• 5–7 years of relevant experience applying machine learning techniques and software development in product or research environments, or equivalent advanced degree experience.
• Experience in interdisciplinary research environments such as academic labs, research institutes, or applied research organizations.
• Demonstrated ability to independently learn and apply new ML and research computing tools, frameworks, and methodologies.
• Prior experience teaching, tutoring, or mentoring others on ML, software engineering, or research computing.
• Extensive experience with ML architectures, familiar with several (e.g. some of CNNs, DNNs, transformers, graph neural networks, diffusion models, GNNs, fusion architectures, multimodal models or reinforcement learning).
• Strong theoretical foundations in linear algebra, calculus, optimization, probability, and statistics for machine learning.
• Expertise with ML/deep learning frameworks (PyTorch, TensorFlow), libraries (scikit-learn), and scientific software development.
• Knowledge of algorithms and data structures to produce efficient, maintainable, well-documented code.
• Skilled in data handling, cleaning, and preprocessing; experience managing structured and unstructured data, relational databases, and SQL.
• Experience developing scalable software for scientific workflows, including web front-ends and back-end services.
• Experience with cloud computing platforms, containerization/orchestration tools for ML workflow management and scalability.
• Specialized knowledge in at least one domain: NLP, computer vision, reinforcement learning, or scientific data integration.
• Excellent written and verbal communication skills for technical and non-technical audiences.
• Advanced interpersonal skills for collaborative work and conflict mediation within multidisciplinary teams.
• Strong organizational skills: planning, prioritization, multitasking, and meeting deadlines.
• Meticulous attention to detail and self-management of time-sensitive workflows.
• Sound judgment in handling sensitive or confidential information.
• Team-oriented, flexible, and willing to support evolving lab and project needs.

Responsibilities

• Architect complex machine learning and scientific computing research projects, including designing scalable front-end and back-end software structures that integrate and accelerate scientific workflows for multi-institutional collaborations.
• Develop, test, debug, and maintain new and existing application software, user interfaces, and back-end services supporting data acquisition, ingestion, and integration from heterogeneous sources (including structured/unstructured datasets and metadata extraction).
• Provide technical guidance in project requirements, documentation, software solution design, architecture, and implementation across research-focused computational projects.
• Design, develop, train, and rigorously evaluate machine learning and deep learning models (CNNs, DNNs, transformers, graph neural networks, diffusion models, multimodal models, reinforcement learning) as well as software solutions for scientific data integration.
• Serve as technical lead, mentoring PhD students and lab researchers on engineering standards, reproducible research practices, advanced ML techniques, and robust software development methodologies.
• Collaborate with faculty to identify, scope, and implement computational and ML-driven solutions aligned with cross-disciplinary research priorities, including strategies for collection, organization, analysis, and display of scientific or geographic data.
• Build robust end-to-end data processing pipelines, including data cleaning, feature engineering, and management for multimodal scientific datasets.
• Integrate cloud platforms, high-performance computing resources, and collaborate with infrastructure teams employing MLOps tools for scalable experimentation and deployment.
• Document and communicate research results via manuscripts, technical reports, conference presentations, and internal or external stakeholder briefings.
• Participate in regular team and project meetings, supporting planning, risk management, milestone coordination, and contributing technical expertise to project feasibility reviews.
• Apply ML and software engineering best practices including version control, testing, technical documentation, and reproducible computation.
• Evaluates new technologies and software products to determine feasibility and desirability of incorporating their capabilities within research projects.
• Works independently to define and document project requirements and provides overall technical guidance in design, architecture and implementation of software solutions.
• Perform other related work as needed.

Benefits

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees