Research Assistant - College of Engineering

Carnegie Mellon University; Pittsburgh (Allegheny County); On-site; USD 2,755,000 - 4,133,000; Part time

Benefits offered by this job

Medical, prescription, dental, and/or视

Tuition benefits

Paid time off

Life and disability insurance

Free Pittsburgh Regional Transit bus  

Family Concierge Team

Fitness center access

Job summary

Carnegie Mellon University’s Department of Mechanical Engineering is seeking a Research Assistant to develop AI/ML methods for scientific lead optimization and laboratory search. You will work on large language model systems that retrieve and synthesize literature, protocols, and experimental data, integrating RAG, embeddings, and tool APIs.

The role emphasizes reproducible research workflows, human-in-the-loop review, and collaboration with interdisciplinary teams to advance AI-enabled science

Qualifications

• Bachelor’s degree in a quantitative field is required.
• Master’s degree or Ph.D. preferred.
• At least 2 years of experience in research, software, or applied ML.
• Hands-on Python and modern deep‑learning frameworks experience.
• Experience with LLM applications, RAG, and reproducible research workflows preferred.
• Experience connecting AI systems to laboratories or experimental workflows desirable.

Responsibilities

• Develop LLM- and agent-based systems for laboratory search that retrieve and synthesize literature, protocols, and data.
• Develop AI/ML methods for lead optimization including generation, ranking, and optimization.
• Build domain-adapted LLM workflows with prompt engineering and integration with predictive models.
• Create evaluation frameworks for scientific accuracy, grounding, and robustness; implement guardrails.
• Integrate AI recommendations with iterative laboratory design-build-test-learn cycles.
• Develop interfaces to query laboratory knowledge and compare candidate leads with provenance.
• Collaborate with scientists and engineers to translate requirements into AI-enabled tools.
• Maintain technical documentation, code quality, and reproducibility standards.

Skills

Python

ML/AI systems

LLM/RAG

Data analysis

Experiment planning

Scientific literature review

Education

Bachelor’s degree in a quantitative field

Master’s degree or Ph.D. preferred

Tools

Vector databases

Deep learning frameworks

Python ML tools

Job description

Carnegie Mellon University is a private, global research university that stands among the world’s most renowned education institutions. With ground-breaking brain science, path-breaking performances, creative start-ups, big data, big ambitions, hands‑on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the curious to deliver work that matters, your journey starts here! Innovation. Interdisciplinary collaboration. Complex problem solving. In Carnegie Mellon University’s Department of Mechanical Engineering, faculty members, researchers, and students are revolutionizing focus areas in advanced manufacturing, bioengineering, computational engineering, energy and the environment, product design, and robotics. In addition, they are using their expertise in interdisciplinary research centers across the university.

Carnegie Mellon University’s department of Mechanical Engineering is searching for a Research Assistant to join their team. This is an exciting opportunity for someone who thrives in an interesting and challenging work environment. In this role, you will develop and apply artificial intelligence and machine learning methods for scientific lead optimization and laboratory search. The position will focus on developing large language model (LLM) and agentic AI systems that can retrieve and synthesize scientific information, reason over experimental data, propose and prioritize candidate leads, and support iterative laboratory decision‑making. The work will integrate LLMs with domain‑specific data, retrieval‑augmented generation, scientific databases, predictive ML models, and experiment‑planning workflows to accelerate discovery while maintaining traceability, evaluation, and appropriate human oversight.

Responsibilities

• Develop LLM- and agent-based systems for laboratory search that retrieve and synthesize scientific literature, protocols, prior experimental results, and structured scientific data using RAG, embeddings, vector databases, and tool/API integrations.
• Develop AI/ML methods for lead optimization, including candidate generation, ranking, property prediction, multi-objective optimization, active learning, and uncertainty-aware prioritization of experiments.
• Build domain-adapted LLM workflows through prompt engineering, structured outputs, fine-tuning or parameter-efficient adaptation when justified, and integration with specialized predictive models and scientific tools.
• Create evaluation frameworks for scientific accuracy, retrieval quality, grounding, hallucination/error rates, robustness, reproducibility, and usefulness of AI recommendations; implement guardrails and human-in-the-loop review for consequential laboratory decisions.
• Integrate AI recommendations with iterative design-build-test-learn or related laboratory cycles, using experimental feedback to update models, refine search strategies, and improve candidate selection.
• Develop interfaces and automation that allow researchers to query laboratory knowledge, compare candidate leads, inspect evidence and provenance, and understand the rationale and uncertainty behind model recommendations.
• Work with multidisciplinary teams to curate datasets, define scientific objectives and constraints, establish benchmarks, and translate laboratory requirements into deployable AI/ML capabilities.
• Track advances in LLMs, scientific foundation models, AI agents, retrieval/search, and AI for science; prototype promising methods and recommend approaches based on measured performance rather than model novelty alone.
• Design, implement, test, and document AI/ML software and research workflows for scientific search, data analysis, prediction, and decision support.
• Analyze experimental, computational, and literature-derived datasets; develop reproducible pipelines; communicate findings to research collaborators and stakeholders.
• Evaluate models using appropriate quantitative benchmarks, error analysis, ablation studies, and validation procedures; identify limitations and opportunities for improvement.
• Collaborate with scientists, engineers, and software developers to translate research needs into usable AI-enabled tools and workflows.
• Maintain technical documentation, code quality, version control, data provenance, and reproducibility standards.
• Contribute to reports, presentations, publications, proposals, and demonstrations as appropriate.
• Other duties as assigned.

Qualifications

• Bachelor’s degree in computer science, machine learning, artificial intelligence, data science, engineering, computational science, chemistry, materials science, bioengineering, or a closely related quantitative field.
• Master’s degree or Ph.D. in a relevant technical or scientific discipline preferred.
• 2 years of experience: Relevant research, software, or applied machine learning experience; advanced graduate research may substitute for professional experience where permitted by the applicable job profile.
• Hands‑on experience developing and evaluating ML/AI systems using Python and modern deep‑learning frameworks.
• Experience with LLM applications, RAG/search systems, scientific or engineering datasets, model benchmarking, and reproducible research workflows is strongly preferred.
• Experience connecting AI systems to laboratories, simulation, or experimental workflows is desirable.
• A combination of education and relevant experience from which comparable knowledge is demonstrated may be considered.

Requirements

• Successful background check.
• Relevant cloud, machine learning, data science, or laboratory/data‑compliance training where applicable preferred.
• Any laboratory safety or institution‑specific training required for access to experimental facilities will be completed as appropriate.

Benefits

• Benefits eligible employees enjoy a wide array of benefits including comprehensive medical, prescription, dental, and vision insurance as well as a generous retirement savings program with employer contributions.
• Unlock your potential with tuition benefits, take well‑deserved breaks with ample paid time off and observed holidays, and rest easy with life and accidental death and disability insurance.
• Additional perks include a free Pittsburgh Regional Transit bus pass, access to our Family Concierge Team to help navigate childcare needs, fitness center access, and much more!

At Carnegie Mellon, we value the whole package when extending offers of employment. Beyond credentials, we evaluate the role and responsibilities, your valuable work experience, and the knowledge gained from education and training. We appreciate your unique skills and the perspective you bring. Your journey with us is about more than just a job; it’s about finding the perfect fit for your professional growth and personal aspirations.

Location Pittsburgh, PA

Job Function Researchers

Position Type Staff – Fixed Term (Fixed Term) Full Time/Part time Part time

Pay Basis Hourly

Prospective Employee Disclosures

For technical assistance, email HR Services or call 412-268-4600.

We are committed to providing an accessible experience throughout our recruitment process. If you need assistance or a reasonable accommodation at any stage of the application, interview, or hiring process, please contact Equal Opportunity Services by email at [e-mail] or by phone at 412-268-5072.