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Engineering Research Associate Jobs in Pennsylvania

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Engineering Research Associate information

What is an engineering research associate?

Engineering Research Associates are professionals who support research and development activities within engineering fields. They assist in designing experiments, collecting and analyzing data, and preparing technical reports and documentation. These associates often work under the supervision of senior engineers or researchers, contributing to projects in areas such as mechanical, electrical, civil, or software engineering. Their work is essential for advancing scientific knowledge and developing new technologies within the engineering sector.

What are the key skills and qualifications needed to thrive as an engineering research associate?

To thrive as an Engineering Research Associate, you need a strong background in engineering principles, analytical research methods, and a relevant degree such as a bachelor's or master's in engineering or a related field. Familiarity with data analysis software, computer-aided design (CAD) tools, and laboratory equipment is typically required, along with experience in technical report writing. Strong problem-solving abilities, attention to detail, and effective communication skills help you excel in collaborative research environments. These skills are crucial for advancing innovative projects, ensuring accurate experimental results, and contributing to the success of research teams.

How does an engineering research associate typically collaborate with other team members and departments?

Engineering Research Associates often work within multidisciplinary teams, collaborating closely with engineers, scientists, and project managers to design experiments, analyze data, and develop prototypes. They may also coordinate with external partners or vendors for specialized testing or material procurement. Effective communication and teamwork are essential, as findings and progress must be regularly shared and integrated into broader project goals. This collaborative environment fosters professional growth and innovation, while also requiring adaptability and strong interpersonal skills.

What is the difference between Engineering Research Associate vs Mechanical Engineer?

AspectEngineering Research AssociateMechanical Engineer
Required CredentialsBachelor's or Master's in Engineering or related field; research experience often preferredBachelor's or Master's in Mechanical Engineering; professional licensure optional
Work EnvironmentResearch labs, academic institutions, R&D departmentsDesign offices, manufacturing plants, project sites
Employer & Industry UsageUniversities, government labs, R&D divisions of companiesManufacturing firms, consulting firms, engineering services

The main difference is that Engineering Research Associates focus on conducting research and developing new technologies in lab or academic settings, often working on experimental projects. Mechanical Engineers typically apply engineering principles to design, analyze, and manufacture mechanical systems in practical environments. Both roles require strong technical skills, but their work settings and primary objectives differ.

Infographic showing various Engineering Research Associate job openings in Pennsylvania as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 22% Part Time, 1% Temporary, and 1% Contract. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution.

Postdoctoral Research Associate - Isayev Lab

Pittsburgh, PA • On-site

Carnegie Mellon University
Colleges, Universities, and Professional Schools • 1 - 10 employees
Learn more about Carnegie Mellon Universityexternal

Full-time

Re-posted 15 days ago

Carnegie Mellon University rating

8.8

Company rating: 8.8 out of 10

Based on 25 frontline employees who took The Breakroom Quiz

Job description

Description
The Isayev Lab at Carnegie Mellon University invites applications for a postdoctoral researcher to lead projects at the interface of computational chemistry, machine learning, reaction mechanism elucidation, and automated molecular discovery. The position is ideal for a candidate who wants to turn deep mechanistic understanding into predictive models and closed-loop discovery workflows.
Our lab develops and applies machine learning methods for computational chemistry, materials science, and molecular discovery, including transferable neural network potentials, generative molecular design, and experiment-automation workflows. The postdoc will work in a collaborative CMU environment spanning computational chemistry, AI, automated experimentation, polymer chemistry, and catalysis.
Research directions may include:
Developing automated DFT / ML workflows for mechanistic studies of photoredox, organometallic, and radical catalytic reactions.
Building predictive models that connect quantum-chemical descriptors, catalyst structure, substrate scope, selectivity, and reaction performance.
Applying AIMNet2 and related ML/QM methods to accelerate conformer search, reaction-path exploration, catalyst screening, and high-throughput mechanistic modeling.
Designing closed-loop computational-experimental campaigns for transition metal catalysis, polymer synthesis, and related catalytic transformations.
Creating reusable, open, well-documented software workflows for reaction data generation, curation, featurization, and model deployment.
Collaborating with experimental groups at CMU and external partners to convert mechanistic hypotheses into experimentally testable predictions.
Qualifications
Desired background:
Ph.D. in chemistry, chemical engineering, materials science, or a related field.
Strong experience in computational reaction mechanisms, especially DFT studies of organic, organometallic, photoredox, radical, or homogeneous catalytic systems.
Fluency with Python and modern scientific computing workflows; experience with Git, HPC clusters, SLURM, Gaussian, ORCA, Q-Chem, xTB, RDKit, ASE, or related tools is highly valued.
Interest in machine learning, statistical modeling, active learning, descriptor development, or data-driven reaction prediction.
Ability to work closely with experimental collaborators and communicate mechanistic insight clearly.
Application Instructions
Applications, including a cover letter and a curriculum vitae indicating your interest and relevant training should be submitted electronically via Interfolio.

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