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Associate Machine Learning Chemistry Jobs in Virginia

Virginia Tech

Postdoctoral Associate

Blacksburg, VA

Postdoctoral Associate Apply now Back to search results Job no: 536321 Work type: Research Faculty ... chemistry, machine learning, and agentic science. This full-time appointment is available ...

Virginia Tech

Postdoctoral Associate

Blacksburg, VA · On-site

Postdoctoral Associate Apply now Back to search results Job no: 536282 Work type: Research Faculty ... AI for cybersecurity, including the use of machine learning and large language models for cyber ...

Virginia Tech

Postdoctoral Associate

Blacksburg, VA · On-site

... Machine Learning (SciML). They will be expected to collaborate with members of the group as well as ... Chemistry, Biology or related fields. PhD must be awarded no more than four years prior to the ...

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Associate Machine Learning Chemistry information

What are the key skills and qualifications needed to thrive as an Associate Machine Learning Chemistry, and why are they important?

To thrive as an Associate Machine Learning Chemistry professional, you need a solid background in chemistry, data analysis, and machine learning, typically supported by a relevant degree such as chemistry, computer science, or a related field. Experience with programming languages like Python, machine learning libraries (e.g., TensorFlow, scikit-learn), and cheminformatics software is highly valued. Strong problem-solving skills, attention to detail, and the ability to communicate complex concepts clearly are crucial soft skills. These competencies enable effective collaboration on interdisciplinary teams and the development of innovative solutions in computational chemistry research.

How does an Associate Machine Learning Chemistry professional typically collaborate with research scientists and engineers?

As an Associate Machine Learning Chemistry professional, you will frequently work alongside research scientists and chemical engineers to develop predictive models and analyze experimental data. Collaboration involves translating chemical problems into machine learning tasks, sharing insights from model results, and participating in interdisciplinary meetings to refine research objectives. Effective communication and teamwork are essential, as you may be required to explain machine learning concepts to non-technical colleagues and integrate their domain expertise into your models. This collaborative environment fosters both scientific discovery and professional growth.

What are Associate Machine Learning Chemists?

Associate Machine Learning Chemists are professionals who combine expertise in chemistry with skills in machine learning to analyze chemical data, develop predictive models, and accelerate scientific discovery. They often work on tasks like predicting molecular properties, optimizing chemical reactions, and supporting drug discovery efforts using computational tools. Typically, these roles require a strong foundation in chemistry, programming experience (often in Python), and familiarity with machine learning libraries. Associate positions are generally entry-level or early-career roles, providing support to senior scientists and data scientists in research and development teams.

What is the difference between Associate Machine Learning Chemistry vs Associate Data Scientist?

AspectAssociate Machine Learning ChemistryAssociate Data Scientist
Required CredentialsBachelor's or Master's in Chemistry, Data Science, or related fields; familiarity with ML frameworksBachelor's or Master's in Data Science, Statistics, Computer Science; programming skills in Python/R
Work EnvironmentResearch labs, pharmaceutical or chemical companies, biotech firmsTech companies, finance, healthcare, consulting firms
Employer & Industry UsageUsed in industries applying ML to chemical data, drug discovery, materials scienceApplied across industries analyzing large datasets, predictive modeling

Associate Machine Learning Chemistry focuses on applying machine learning techniques specifically to chemical and scientific data, often within research or pharmaceutical settings. In contrast, Associate Data Scientist has a broader scope, working with various data types across multiple industries. Both roles require strong analytical skills and familiarity with ML tools, but their industry focus and data types differ.

What are the most commonly searched types of Machine Learning Chemistry jobs in Virginia? The most popular types of Machine Learning Chemistry jobs in Virginia are:
What are popular job titles related to Associate Machine Learning Chemistry jobs in Virginia? For Associate Machine Learning Chemistry jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Associate Machine Learning Chemistry jobs in Virginia look for? The top searched job categories for Associate Machine Learning Chemistry jobs in Virginia are:
What cities in Virginia are hiring for Associate Machine Learning Chemistry jobs? Cities in Virginia with the most Associate Machine Learning Chemistry job openings:
Associate Machine Learning Chemistry jobs near you
Infographic showing various Associate Machine Learning Chemistry job openings in Virginia as of May 2026, with employment types broken down into 1% As Needed, 65% Full Time, 32% Part Time, and 2% Contract. Highlights an 91% Physical, and 9% Remote job distribution.

Associate Machine Learning Engineer - Secure AI Lab

Software Engineering Institute | Carnegie Mellon University

Arlington, VA • On-site

Full-time

Posted 20 days ago

Job description

Job Summary:
Carnegie Mellon University’s Software Engineering Institute is conducting cutting-edge research in applied artificial intelligence and engineering solutions for AI technologies. The Associate Machine Learning Engineer will focus on improving the security and robustness of AI systems, while collaborating with a team to solve practical engineering problems for government clients.
Responsibilities:
• Building Machine Learning Models and Systems: You will work with machine learning frameworks such as TensorFlow, PyTorch, Torch, and Caffe and modern programming languages including Python, C/C++, and Java. You will build and work with data pipelines, ETL processes, and backend systems. You will work with, extend, and implement state-of-the-art machine learning methods.
• Technical Experimentation: You will experiment with modern and emerging machine learning frameworks, methods, and algorithms in application domains that include computer vision, natural language processing, planning and scheduling, robot control, and engineering safe, trusted, and reliable machine learning systems.
• Testing and evaluation. You'll conduct rapid prototyping to demonstrate and evaluate technologies in relevant environments. You'll evaluate systems for performance and security. You'll test capabilities using novel testing and analysis techniques.
• Collaboration. You'll actively participate on teams of developers, researchers, designers, and technical leads. You'll collaborate with researchers and our government customers to understand challenges, needs, and possible solutions.
• Mentoring. You'll contribute to improving the overall technical capabilities of the Division by mentoring and teaching others, participating in design (software and otherwise) sessions, and sharing insights and wisdom across the SEI.
Qualifications:
Required:
• A bachelor’s degree in computer science, statistics, machine learning, electrical engineering, or related discipline with three (3) years of experience; OR MS in the same fields with one (1) year of experience; OR PhD in a relevant discipline.
• Willingness to work onsite 5 days per week at SEI offices in Pittsburgh, PA or Arlington, VA.
• You will be subject to a background investigation and must be able to obtain and maintain an active Department of War security clearance.
• Willing to travel up to 25% of the time to locations outside of your home location. Travel sites include SEI offices in Pittsburgh and Washington, D.C., sponsor sites, and conferences.
• Comprehensive knowledge of machine learning; previous experience in adversarial machine learning desirable but not required.
• A track record of using well-established engineering practices to solve difficult problems.
• An understanding of how to convert research results into functioning prototypes or capabilities.
• Experience leading technical projects in novel areas with limited previous work to build upon.
• Strong written and verbal communication skills; able to convey complex technical ideas in a layperson’s terms.
• Ample experience with publishing written or technical artifacts showcasing your work.
• Strong collaboration skills for working with colleagues and sponsors.
• Willingness to guide and mentor junior team members.
Company:
We conduct cutting-edge research and development that accelerates the transition of technology to the Department of War (DoW), delivering measurable impact in support of the national security mission. Founded in 1984, the company is headquartered in Pittsburgh, USA, with a team of 501-1000 employees. The company is currently Late Stage.

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