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Scientific Machine Learning Jobs in Virginia (NOW HIRING)

If cutting edge data science projects resonate with you, and you care deeply about joining a ... As a Machine Learning Engineer, you'll be part of the Agile team delivering machine learning ...

Machine Learning Engineer

Arlington, VA · On-site

$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 ...

If cutting edge data science projects resonate with you, and you care deeply about joining a ... As a Machine Learning Engineer, you'll be part of the Agile team delivering machine learning ...

If cutting edge data science projects resonate with you, and you care deeply about joining a ... As a Machine Learning Engineer, you'll be part of the Agile team delivering machine learning ...

If cutting edge data science projects resonate with you, and you care deeply about joining a ... As a Machine Learning Engineer, you'll be part of the Agile team delivering machine learning ...

We are seeking a Machine Learning Engineer to join our team at MORSE. You will play a pivotal role ... D. in Computer Science, Computer Engineering, Data Science, Aerospace, Mathematics, Physics, or ...

... 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 ...

... 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 ...

... 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 ...

... 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 ...

... 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 ...

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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 cities in Virginia are hiring for Scientific Machine Learning jobs? Cities in Virginia with the most Scientific Machine Learning job openings:
Machine Learning Engineer

Machine Learning Engineer

NT Concepts

Herndon, VA • On-site, Remote

Other

Posted 22 days ago


Job description

NTC OVERVIEW:  We are seeking a Machine Learning Engineer to join our team. Working at NT Concepts means that you are part of an innovative, agile company dedicated to solving the most critical challenges in National Security. We're looking for the best and the brightest to join us in supporting this mission. If meaningful work, initiative, creativity, and continuous self-improvement are important to your career, join our growing team and discover What's Next for you.

Mission Focus: As a Machine Learning Engineer, you will have the unique opportunity to support research, design, and implement cutting edge algorithms for a program focused on building robust computer vision algorithms. This requires coding in Python with PyTorch, implementing and maintaining development environments and supporting ML tools, such as Kubeflow and MLFlow. Additionally, you will contribute to the program's source code, implementing data science techniques.

Our delivery teams are driven to explore new ideas and technology, and care deeply about collaboration, feedback, and iteration. We follow modern agile practices, embrace the Ops ethos (DataOps/DevSecOps/MLOps) to "automate-first", use modern tech stacks, and constantly challenge each other to grow and improve. 

If cutting edge data science projects resonate with you, and you care deeply about joining a mission-driven company with a strong growth direction and diverse culture, we'd love to learn more about you. Check out the details below, and let's connect. 

Technical members of our solutions teams require little guidance, but love to learn, collaborate, and problem solve. This position requires mid to senior level of experience, a passion for mission support, and a strong desire to solve our customers' hardest technical and data challenges. 

Clearance: TS/SCI Clearance required.

Location/Flexibility: Vienna, VA and Chantilly, VA with remote flexibility

Responsibilities: 

As a Machine Learning Engineer, you'll be part of the Agile team delivering machine learning applications and software systems at scale. You'll implement computer vision machine learning applications using existing and emerging technology platforms to deliver business value to our clients. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. You'll also mentor other engineers and develop your technical knowledge and skills to keep our team at the cutting edge of technology.

  • Collaborate with a cross-functional team comprising other ML Engineers, Software Engineers, DevSecOps Engineers, and Data Scientists.
  • Develop machine learning models and pipelines that are integral to mission success within this computer vision platform.

Qualifications:

  • 4+ years of relevant hands-on experience developing and implementing ML algorithms
  • Practical experience training and deploying Machine Learning models. Ideal candidate would have experience with PyTorch, NumPy, TensorFlow, VS Code)
  • Understanding of machine learning techniques and algorithms, data mining, and statistical analysis.
  • Experience with cloud platform (AWS, Azure and GCP), AWS experience preferred
  • Proven experience with modern software development and engineering practices including scrum/agile, Git, and DevSecOps specifically GitLab CI/CD
  • Experience building and maintaining machine learning pipelines
  • Experience with container applications such as Docker, Kubernetes, OpenShift. Kubernetes is preferred
  • Practical programming and scripting skills (Python preferred)
  • Understanding of data structures, data modeling and software architecture.
  • A passion for (and track record of) innovation, an interest in exploring and leveraging new data modalities, and working across interdisciplinary teams
  • Experience with synthetic data generation for training and evaluation of ML Models is a plus
  • Experience working with customers to better optimize their ML objectives
  • Fast learner, analytical thinker, creative, hands-on, strong communication skills
  • Able to work both independently and as part of a team
  • Excellent problem-solving skills and attention to detail
  • Experience working with LLMs for problem solving and code production in a safe and responsible manner

Physical Requirements: 

  • Prolonged periods sitting at a desk and working on a computer.
  • Must be able to lift up to 10-15 pounds at times.

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