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Machine Learning Infrastructure Engineer Jobs in Washington

Kitware

Machine Learning Engineer

Arlington, VA · Hybrid

Machine learning experience using visual data * Understanding of a variety of machine learning ... infrastructure. Our customers and collaborators include top universities from around the world ...

Kitware

Machine Learning Engineer

Arlington, VA · Hybrid

$110K - $160K/yr

Machine learning experience using visual data * Understanding of a variety of machine learning ... infrastructure. Our customers and collaborators include top universities from around the world ...

Kitware

Machine Learning Engineer

Arlington, VA · On-site

$110K - $160K/yr

Machine learning experience using visual data * Understanding of a variety of machine learning ... infrastructure. Our customers and collaborators include top universities from around the world ...

Cymertek

Machine Learning Engineer

Chantilly, VA · On-site

Machine Learning Engineer LOCATIONChantilly, VA 20151 CLEARANCETS/SCI Full Poly (Please note this position requires full U.S. Citizenship) KEY SUMMARYWe are seeking a talented and innovative Machine ...

Cymertek

Machine Learning Engineer

Tysons, VA · On-site

Machine Learning Engineer LOCATIONTysons, VA 22182 CLEARANCETS/SCI Full Poly (Please note this position requires full U.S. Citizenship) KEY SUMMARYWe are seeking a talented and innovative Machine ...

Cymertek

Machine Learning Engineer

Reston, VA · On-site

Machine Learning Engineer LOCATIONReston, VA 20190 CLEARANCETS/SCI Full Poly (Please note this position requires full U.S. Citizenship) KEY SUMMARYWe are seeking a talented and innovative Machine ...

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Machine Learning Infrastructure Engineer information

What are some common challenges faced by Machine Learning Infrastructure Engineers, and how can these be addressed on the job?

Machine Learning Infrastructure Engineers often face challenges such as ensuring infrastructure scalability, managing resource allocation, and maintaining system reliability while supporting rapid experimentation by data science teams. Balancing the needs for flexibility in research environments with production-grade stability requires a deep understanding of both engineering best practices and the unique requirements of machine learning workflows. Collaboration with data scientists, clear communication about infrastructure capabilities, and staying current with fast-evolving technologies are key strategies for success. Most companies encourage ongoing learning and provide opportunities to contribute to architecture decisions, which makes this a rewarding environment for problem-solvers and innovators.

What are the key skills and qualifications needed to thrive in the Machine Learning Infrastructure Engineer position, and why are they important?

To thrive as a Machine Learning Infrastructure Engineer, you need a strong background in computer science, cloud computing, distributed systems, and experience with machine learning frameworks, often supported by a degree in a related field. Familiarity with tools such as Docker, Kubernetes, Terraform, as well as cloud platforms like AWS, GCP, or Azure, and certifications in cloud or DevOps technologies are highly valued. Strong problem-solving abilities, effective communication, and collaboration skills help engineers work seamlessly with data scientists and cross-functional teams. These skills are essential to design, implement, and maintain robust, scalable infrastructure that enables efficient machine learning development and deployment.

What is a Machine Learning Infrastructure Engineer job?

A Machine Learning Infrastructure Engineer designs, builds, and maintains the systems that support the development and deployment of machine learning models. This includes managing data pipelines, optimizing model training and inference, and ensuring scalability and reliability in production environments. They work closely with data scientists, ML engineers, and DevOps teams to create efficient workflows and infrastructure. Key technologies often include cloud platforms, containerization, orchestration tools, and distributed computing frameworks.

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

Machine Learning Engineer

Virtualitics, Inc

Washington, DC

Full-time

Posted 8 days ago

Job description

Virtualitics is the category leader in AI-native readiness applications for defense, government, and critical infrastructure. Founded on a decade of Caltech research in partnership with NASA/JPL, we are led by scientists, strategists, and servicemembers united by a single mission: to solve the world’s most complex, mission-critical challenges with AI. 

Our Readiness AI solutions deliver operational certainty — giving leaders and operators a clear picture of what’s ready, what’s at risk, and what to do next. By identifying risks early, diagnosing root causes, and recommending prioritized actions with transparent, explainable AI, we help organizations move from data complexity to decision advantage. 

Behind that impact is relentless innovation. Inventors at heart, we hold 15+ U.S. patents and are leading the shift toward agent-driven readiness. But what truly sets us apart is our culture — relentless about results, grounded in transparency, and driven by compassion for the mission and the people it serves.

If you’re motivated by impact, inspired by technical depth, and ready to build AI that performs where it matters most — you’ll find your mission here.

 
Machine Learning Engineer - US TS/SCI Clearance (DC Metropolitan Area)
 
Virtualitics is trailblazing Intelligent Exploration and Enterprise AI with our cutting-edge AI Platform. We are hiring an ML Engineer with the capability and readiness to obtain a U.S.-government security clearance. This role is pivotal in bridging the worlds of machine learning, data engineering, and software development to enhance our AI data applications. Career advancement opportunities are available for those interested
in senior engineering positions and technical leadership.
 

As an ML Applications Engineer, you will:

  • Spearhead platform upgrades, ensuring our products are at the forefront of innovation and effectiveness.

  • Craft and manage dynamic dashboards using the Virtualitics AI Platform Python SDK, transforming data into intuitive visuals for decision-making.

  • Optimize data access patterns, enhancing the efficiency and performance of our AI solutions.

  • Tackle runtime performance issues, ensuring high responsiveness and stability of applications.

  • Architect robust, scalable, and user-friendly applications, considering current trends and future growth.

  • Collaborate closely with Technical Product Managers to drive usability enhancements, ensuring our products meet and exceed user expectations.

Requirements:

  • A degree in Computer Science or related field, or 4+ years of software engineering experience.

  • Must have a TS/SCI security clearance.

  • Must be willing to travel and work from a SCIF as needed.

  • Proven track record of deploying software into production environments.

  • Proficiency in Python with a solid understanding of Python Data Stack (pandas, NumPy, scikit-learn, PyTorch, Matplotlib, etc.).

  • Experience with big data technologies and frameworks (Spark, Databricks, Snowflake, etc).

  • Familiarity with Docker, Kubernetes, and Git.

  • Exceptional problem-solving skills and a keen sense of ownership.

  • Excellent communication skills in English, both written and verbal.

Pluses:

  • Experience in Machine Learning Engineering roles and the end-to-end lifecycle of AI applications, from model development to deployment.

  • Experience with Predictive Maintenance, Supply Chain, Scheduling Optimization, etc.

  • Experience with PCAP and network monitoring, CVEs and Cyber Vulnerabilities, etc. 

  • 1 year of experience with technologies like task schedulers (e.g. Celery, Airflow, Prefect, etc.) and web-app development stacks (e.g. Flask/Django) or app building kits like Streamlit/Plotly Dash.

Compensation and Benefits:

  • Competitive salary/equity/bonus based on experience and education.

  • Comprehensive benefits package including medical, dental, and vision.

  • Unlimited paid time off.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.