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Scientific Machine Learning Jobs in Washington (NOW HIRING)

AISquared

Machine Learning Engineer

Washington, DC · On-site

... Machine Learning Engineer to join their core AI team. In this role, you will be responsible for ... scientists and product teams to ensure reliable and efficient solutions. Responsibilities : • ...

System One

Machine Learning/AI Engineer

Merrifield, VA · Remote

This position is for a Machine Learning/AI Engineer or Data Scientist focused on designing, developing, deploying, and securing advanced AI/ML solutions (more of an AI/ML Engineer and less of data ...

System One

Machine Learning/AI Engineer

Merrifield, VA · Remote

This position is for a Machine Learning/AI Engineer or Data Scientist focused on designing, developing, deploying, and securing advanced AI/ML solutions (more of an AI/ML Engineer and less of data ...

System One

Machine Learning/AI Engineer

Merrifield, VA · Remote

This position is for a Machine Learning/AI Engineer or Data Scientist focused on designing, developing, deploying, and securing advanced AI/ML solutions (more of an AI/ML Engineer and less of data ...

Booz Allen Hamilton

Machine Learning Engineer

Arlington, VA · On-site

$77K - $176K/yr

You Have: * 2+ years of experience with artificial intelligence, data science, or machine learning engineering, including developing and deploying models * Experience with Python coding and libraries ...

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Scientific Machine Learning information

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 are popular job titles related to Scientific Machine Learning jobs in Washington?

For Scientific Machine Learning jobs in Washington, the most frequently searched job titles are:

What job categories do people searching Scientific Machine Learning jobs in Washington look for?

The top searched job categories for Scientific Machine Learning jobs in Washington are:

What cities in Washington are hiring for Scientific Machine Learning jobs?

Cities in Washington with the most Scientific Machine Learning job openings:

Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in Washington as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 18% Part Time, 2% Temporary, and 5% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution.

Machine Learning Engineer

AISquared

Washington, DC • On-site

Full-time

Re-posted 2 days ago

Job description

Job Summary:
aisquared is a company focused on AI solutions, and they are seeking a highly skilled Machine Learning Engineer to join their core AI team. In this role, you will be responsible for deploying, maintaining, and monitoring AI/ML systems, while collaborating with data scientists and product teams to ensure reliable and efficient solutions.
Responsibilities:
• Design, implement, and maintain ML deployment pipelines for scalable production systems.
• Operationalize large language models (LLMs) and other AI/ML models, ensuring high availability and reliability.
• Build robust model monitoring, logging, and alerting systems to track performance and detect drift.
• Partner with data scientists to transition models from research/prototype into production-ready deployments.
• Develop CI/CD pipelines for ML workflows, integrating testing, validation, and automated deployment.
• Optimize runtime performance of ML models across cloud platforms (AWS, GCP, Azure) and distributed systems.
• Apply containerization and orchestration (Docker, Kubernetes) to enable reproducible, scalable systems.
• Collaborate with cross-functional teams to ensure ML systems align with platform goals and business requirements.
Qualifications:
Required:
• 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or similar role.
• Proven experience deploying and maintaining machine learning models in production at scale.
• Hands-on experience with ML lifecycle tooling (MLflow, Kubeflow, SageMaker, Vertex AI, or similar).
• Strong proficiency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow.
• Deep knowledge of containerization (Docker) and orchestration (Kubernetes) for production ML systems.
• Expertise with cloud platforms (AWS, GCP, Azure) for ML deployment and scaling.
• Strong understanding of MLOps best practices, monitoring, and automation.
• Excellent problem-solving skills, with an emphasis on building reliable, scalable systems.
• Strong communication and collaboration skills across technical and non-technical teams.
Company:
AISquared is a SaaS that provides AI model integration, real-time insights, data analytics, and optimization services. Founded in 2021, the company is headquartered in Mountain View, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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