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Scientific Machine Learning Jobs in Chester, VA (NOW HIRING)

WorkNovas LLC

Machine Learning Engineer

Richmond, VA · On-site

... Science, Engineering, or a related field 5+ years of experience in machine learning, AI engineering, or applied ML Strong proficiency in Python for ML and backend development Hands-on experience ...

Varsity Tutors

Machine Learning Tutor

Richmond, VA · Remote

$40/hr

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Capital One

Lead Machine Learning Engineer

Richmond, VA · On-site

$101K - $133K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar ...

Capital One

Lead Machine Learning Engineer

Richmond, VA

$101K - $133K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar ...

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All JobsScientific Machine Learning JobsScientific Machine Learning Jobs in Chester, VA

Scientific Machine Learning information

See Chester, VA salary details

$12

$28

$48

How much do scientific machine learning jobs pay per hour?

As of Sep 10, 2026, the average hourly pay for scientific machine learning in Chester, VA is $28.97, according to ZipRecruiter salary data. Most workers in this role earn between $17.69 and $36.92 per hour, depending on experience, location, and employer.

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are the key skills and qualifications needed to thrive as a scientific machine learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What are some common challenges faced by professionals in scientific machine learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

What job categories do people searching Scientific Machine Learning jobs in Chester, VA look for?

The top searched job categories for Scientific Machine Learning jobs in Chester, VA are:

What cities near Chester, VA are hiring for Scientific Machine Learning jobs?

Cities near Chester, VA with the most Scientific Machine Learning job openings:

Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in Chester, VA as of June 2026, with employment types broken down into 3% As Needed, 68% Full Time, 26% Part Time, and 3% Contract. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution, with an average salary of $60,249 per year, or $29 per hour.

AI Research Scientist - Machine Learning

Richmond, VA • On-site

AIToolboard
See more jobs at AIToolboardexternal

Other

Posted 22 days ago

Job description

Jobs / AI Research Scientist - Machine Learning

AI Research Scientist - Machine Learning

Full-time

About the Role

Our client is seeking a brilliant and innovative AI Research Scientist specializing in Machine Learning to join their cutting-edge R&D team in Richmond, Virginia, US. This role is at the forefront of developing next-generation AI technologies and algorithms. You will be responsible for designing, implementing, and evaluating advanced machine learning models, conducting groundbreaking research, andcontributing to high-impact AI applications. The ideal candidate possesses a strong academic background, a deep understanding of ML principles, and a passion for pushing the boundaries of artificial intelligence.Key Responsibilities:Conduct advanced research in machine learning, deep learning, and related AI fields. Design, develop, and implement novel algorithms and models for complex AI problems. Experiment with various ML techniques, including supervised, unsupervised, reinforcement learning, and neural networks. Analyze large datasets, preprocess data, and extract meaningful features for model training. Evaluate model performance, identify areas for improvement, and iterate on designs. Collaborate with software engineers to deploy and integrate AI models into production systems. Stay current with the latest advancements in AI and ML research through literature review and conference participation. Publish research findings in leading scientific journals and present at conferences. Mentor junior researchers and interns, fostering a collaborative research environment. Contribute to the intellectual property portfolio through patent applications.Qualifications:Ph.D. or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Statistics, or a related quantitative field. Proven research experience demonstrated through publications in top-tier AI/ML conferences and journals (e.g., NeurIPS, ICML, ICLR, CVPR). Strong theoretical foundation in machine learning, deep learning, and statistical modeling. Proficiency in programming languages such as Python, and experience with ML libraries like TensorFlow, PyTorch, scikit-learn. Experience with data manipulation and analysis tools. Ability to design and conduct rigorous experiments, interpret results, and draw insightful conclusions. Excellent problem-solving skills and creativity in developing novel solutions. Strong communication and presentation skills, with the ability to articulate complex technical concepts. Experience with distributed computing frameworks (e.g., Spark) is a plus. Experience in specific domains like NLP, computer vision, or reinforcement learning is highly desirable. Join a forward-thinking team that is shaping the future of AI. This exciting opportunity is based in Richmond, Virginia, US .

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