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Machine Learning Infrastructure Engineer Jobs in Washington

We are seeking a Machine Learning Engineer with a passion for building mission-critical ... systems and secure cloud infrastructures. * Optimization & Governance: Optimize inference ...

Machine Learning/AI Engineer Location: Hybrid in Vienna, VA or Remote Pay Rate: Open to Both W2 and ... Develop data science solutions based on tools and cloud computing infrastructure. * Perform other ...

Machine Learning/AI Engineer Location: Hybrid in Vienna, VA or Remote Pay Rate: Open to Both W2 and ... Develop data science solutions based on tools and cloud computing infrastructure. * Perform other ...

Machine Learning/AI Engineer Location: Hybrid in Vienna, VA or Remote Pay Rate: Open to Both W2 and ... Develop data science solutions based on tools and cloud computing infrastructure. * Perform other ...

Design, implement, and maintain cloud-native infrastructure and deployment pipelines using ... of machine learning/statistical modeling data analysis tools and techniques Preferred ...

Design, implement, and maintain cloud-native infrastructure and deployment pipelines using ... of machine learning/statistical modeling data analysis tools and techniques Preferred ...

Role Summary The Machine Learning Engineer is responsible for developing and implementing machine learning models and algorithms to solve complex problems. Main Responsibilities and Duties * Develop ...

The Machine Learning Engineer is responsible for developing and implementing machine learning models and algorithms to solve complex problems. Main Responsibilities and Duties: Develop and implement ...

Machine Learning Engineer

Chantilly, VA ยท On-site

$120K - $180K/yr

We are seeking a Machine Learning Engineer with a passion for building mission-critical ... systems and secure cloud infrastructures. * Optimization & Governance: Optimize inference ...

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Machine Learning Infrastructure Engineer information

What is a machine learning infrastructure engineer?

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 the key skills and qualifications needed to thrive as a machine learning infrastructure engineer?

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 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 cities in Washington are hiring for Machine Learning Infrastructure Engineer jobs?

Cities in Washington with the most Machine Learning Infrastructure Engineer job openings:

Infographic showing various Machine Learning Infrastructure Engineer job openings in Washington as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 73% Full Time, 22% Part Time, and 3% Contract. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution.

Senior Machine Learning Engineer, Public Sector

Washington, DC โ€ข On-site

Segment (Twilio)
Internet and ITย โ€ขย 501 - 1,000 employees

$225K - $282K/yr

Other

Medical, Dental, Vision, Retirement, PTO

Posted 27 days ago


Key responsibilities

  • Take state-of-the-art models developed internally and from the community, and use them in production to solve problems for customers and taskers

  • Improve and maintain production models through retraining, hyperparameter tuning, and architectural updates

  • Collaborate with product and research teams to identify and prototype ML-driven product enhancements


Job description

Senior Machine Learning Engineer, Public Sector

Washington, DC

The goal of a Senior Machine Learning Engineer at Scale is to leverage techniques in the fields of generative AI, computer vision, reinforcement learning, and agentic AI to improve Scale's products and customer experience in production environments. Our machine learning engineers take advantage of robust internal infrastructure and unique access to massive datasets to deliver improvements to our customers.

Our Public Sector Machine Learning team is focused on deploying cutting-edge models to mission-critical government systems through products like Donovan and Thunderforge . Our work spans multiple modalities, with a strong focus on both large language models and computer vision. On the LLM side, we are developing agentic systems that help solve complex operational and planning challenges for government partners. This includes building agent frameworks that integrate with custom retrieval pipelines and production APIs, as well as evaluation tools to benchmark and refine agent behavior. We're also advancing research in areas like reinforcement learning for agentic LLMs, with successful deployment into real-world operational environments. On the computer vision front, we're training advanced models to increase labeling throughput and automate perception tasks. Our efforts include building large-scale fine-tuning pipelines, training models across multiple modalities, and developing generalizable vision foundation models to support a wide range of defense applications.

You will:
  • Take state of the art models developed internally and from the community, use them in production to solve problems for our customers and taskers
  • Improve and maintain production models through retraining, hyperparameter tuning, and architectural updates, while preserving core performance characteristics
  • Collaborate with product and research teams to identify and prototype ML-driven product enhancements, including for upcoming product lines
  • Work with massive datasets to develop both generic models as well as fine tune models for specific products
  • Build scalable machine learning infrastructure to automate and optimize our ML services
  • Serve as a cross-functional representative and advocate for machine learning techniques across engineering and product organizations
  • Be comfortable learning new technologies quickly and managing multiple priorities in a fast-paced environment
  • Comfortable with light travel (approximately 10%) for customer interaction and team needs
  • This role will require an active security clearance
Ideally Youโ€™d Have:
  • Extensive experience with GenAI, Agentic AI, natural language processing, deep learning and deep reinforcement learning, or computer vision in a production environment
  • Solid background in algorithms, data structures, and object-oriented programming
  • Strong programing skills in Python, experience in Tensorflow or PyTorch
Nice to Haves:
  • Graduate degree in Computer Science, Machine Learning or Artificial Intelligence specialization
  • Experience working with cloud platforms (eg. AWS or GCP) and deploying machine learning models in cloud environments
  • Experience with computer vision, generative AI models, large language models, or agentic systems
  • Familiarity with ML evaluation frameworks and agentic model design

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.

The base salary range for this full-time position in the location of Washington DC is:

$225,750 - $282,450 USD

PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.

About Us:

At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.

We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.

We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information.

We comply with the United States Department of Labor's Pay Transparency provision.

PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicantsโ€™ needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our privacy policy for additional information.

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