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Scientific Machine Learning Jobs in Virginia (NOW HIRING)

Ametek

Machine Learning Engineer

Herndon, VA · On-site

Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Engineering, Mathematics, or a closely related quantitative field. * This is a hybrid role in Herndon ...

Steampunk

Machine Learning Engineer

Mclean, VA

$115K - $150K/yr

Overview We are looking for seasoned Machine Learning Engineer to work with our existing team of Data Scientists and Engineers to use AI/ML technology in supporting Federal use cases. We are looking ...

SAIC

Machine Learning Developer

Arlington, VA · Hybrid

The ideal candidate will have a strong background in computer science, software engineering, and experience with machine learning algorithms and frameworks. The Machine Learning Developer will ...

Cymertek

Machine Learning Engineer

Chantilly, VA

Machine Learning Engineer LOCATIONChantilly, VA 20151 CLEARANCETS/SCI Full Poly (Please note this ... You will collaborate with data scientists, engineers, and product teams to turn data into ...

Cymertek

Machine Learning Engineer

Tysons, VA

Machine Learning Engineer LOCATIONTysons, VA 22182 CLEARANCETS/SCI Full Poly (Please note this ... You will collaborate with data scientists, engineers, and product teams to turn data into ...

Cymertek

Machine Learning Engineer

Reston, VA · On-site

Machine Learning Engineer LOCATION Reston, VA 20190 CLEARANCE TS/SCI Full Poly (Please note this ... You will collaborate with data scientists, engineers, and product teams to turn data into ...

Cymertek

Machine Learning Engineer

Tysons, VA · On-site

Machine Learning Engineer LOCATION Tysons, VA 22182 CLEARANCE TS/SCI Full Poly (Please note this ... You will collaborate with data scientists, engineers, and product teams to turn data into ...

Cymertek

Machine Learning Engineer

Chantilly, VA · On-site

Machine Learning Engineer LOCATION Chantilly, VA 20151 CLEARANCE TS/SCI Full Poly (Please note this ... You will collaborate with data scientists, engineers, and product teams to turn data into ...

Cymertek

Machine Learning Engineer

Reston, VA

Machine Learning Engineer LOCATIONReston, VA 20190 CLEARANCETS/SCI Full Poly (Please note this ... You will collaborate with data scientists, engineers, and product teams to turn data into ...

Kitware

Machine Learning Engineer

Arlington, VA · Hybrid

$110K - $160K/yr

Bachelor's degree or Master's degree in Computer Science, Electrical and Computer Engineering, or ... Machine learning experience using visual data * Understanding of a variety of machine learning ...

Kitware

Machine Learning Engineer

Arlington, VA · Hybrid

Bachelor's degree or Master's degree in Computer Science, Electrical and Computer Engineering, or ... Machine learning experience using visual data * Understanding of a variety of machine learning ...

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Scientific Machine Learning information

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 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 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 are popular job titles related to Scientific Machine Learning jobs in Virginia? For Scientific Machine Learning jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Scientific Machine Learning jobs in Virginia look for? The top searched job categories for Scientific Machine Learning jobs in Virginia are:
What cities in Virginia are hiring for Scientific Machine Learning jobs? Cities in Virginia with the most Scientific Machine Learning job openings:
Scientific Machine Learning jobs near you
Machine Learning Engineer

Machine Learning Engineer

Ametek

Herndon, VA • On-site

Full-time

Posted 23 days ago

AMETEK rating

7.9

Company rating: 7.9 out of 10

Based on 43 frontline employees who took The Breakroom Quiz

51st of 137 rated electronics manufacturers

Job description

We are seeking an early-career Machine Learning Engineer who is excited to grow rapidly by building and deploying production-grade ML systems. The ideal candidate has a strong engineering mindset, has contributed to shipping ML features or products end-to-end, and is eager to take ownership across the full lifecycle-from data pipelines to model design to deployment, monitoring, and iteration in real-world environments.
This role offers hands-on exposure to applied ML, working with IoT datasets, user needs, and product requirements to build scalable solutions that deliver measurable customer ROI.
Responsibilities:
  • Design, build, and deploy ML models into production environments, ensuring reliability, scalability, and performance.
  • Ability to select and apply the appropriate ML approach for a given problem - including supervised learning (e.g., logistic regression, random forest, gradient boosting), unsupervised learning (e.g., clustering, dimensionality reduction), and deep learning techniques when appropriate.
  • Develop and maintain feature engineering pipelines, data preprocessing flows, and training workflows.
  • Collaborate with cross-functional partners including product, data engineering, DevOps & QA to deliver end-to-end ML solutions.
  • Work with DevOps team to implement robust MLOps practices, including versioning, CI/CD for ML, monitoring/alerting, automated retraining, and model governance.
  • Continuously evaluate and improve models by monitoring performance, identifying and addressing bias, detecting data or concept drift, and iterating on features, algorithms, or training processes to maintain reliability over time.
  • Ensure solutions meet security, compliance, and data privacy standards.
  • Document system architectures, modeling decisions, and operational procedures.
  • Work in a high performing scrum team to deliver quality code for stakeholders.

Qualifications - Must Have Skills:
  • 3+ years of professional experience as an ML Engineer, Applied Scientist, or Data Scientist with an emphasis on hands-on software engineering responsibilities, particularly around productionizing models.
  • Demonstrated contributions to shipping ML models into production-not just prototypes-and supporting their maintenance over time.
  • Proficiency in Python and ML frameworks such as PyTorch and Scikit-learn.
  • Prior hands-on experience with cloud platforms (AWS, Azure, GCP) and ML services (e.g., SageMaker, Vertex AI, Azure ML).
  • Familiarity with GenAI system components and architecture, including vector databases, LLM fine-tuning, embeddings pipelines, and retrieval-augmented systems (RAG).
  • Experience with MLOps tooling: Docker, Kubernetes, MLflow, Feature Stores, CI/CD pipelines is preferred.
  • Strong understanding of data structures, algorithms, software engineering fundamentals, and distributed systems concepts.
  • Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Engineering, Mathematics, or a closely related quantitative field.
  • This is a hybrid role in Herndon, VA and no relocation assistance is able to be provided.

Other Beneficial Skills:
  • Familiarity with emerging Agentic AI concepts.
  • Familiarity with Edge ML patterns.
  • Experience working with large-scale data pipelines using Spark, Flink, Beam, or similar frameworks.
  • Experience or demonstrated interest in Vision ML, with familiarity in common vision models and techniques for image classification, object detection, and segmentation.
  • Knowledge of observability and monitoring tools for ML systems (Prometheus, Grafana, etc.)
  • Experience with cloud infrastructure and managing resources in the cloud.
  • Master's degree in a relevant field may be considered equivalent to up to 2 years of professional ML engineering experience, particularly when supported by hands-on coursework, research, internships, or real-world projects involving applied machine learning.

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Compensation
Employee Type: Salaried
Currency: USD
Salary Minimum: 130,000
Salary Maximum: 155,000
Incentive: No
Disclaimer: Where a specific pay range is noted, it is a good faith estimate at the time of this posting. The actual salary offered will be based on experience, skills, qualifications, market / business considerations, and geographic location.
For more information on AMETEK's competitive benefits, please click here.
AMETEK, Inc. is a leading global provider of industrial technology solutions serving a diverse set of attractive niche markets with annual sales over $7.5 billion.
AMETEK is committed to making a safer, sustainable, and more productive world a reality. We use differentiated technology solutions to solve our customers' most complex challenges. We employ 22,000 colleagues, in 35 countries, that are grounded by our core values: Ethics and Integrity, Respect for the Individual, Inclusion, Teamwork, and Social Responsibility. AMETEK is a component of the S&P 500. Visit https://www.ametek.com/careers for more information.
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class. Individuals who need a reasonable accommodation because of a disability for any part of the employment process should call 1 (866) 263-8359.

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