N

Noblis

Nonprofit federal contractor delivering systems engineering

AI/ML Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeReston, VA, USA$90.7k - $251.3k /yr

About the Job

Requirements

  • Active Top Secret/SCI clearance with polygraph is required.
  • U.S. citizenship is required.
  • Travel within the United States up to 10% is required.
  • The role requires lifting up to 30 pounds, walking, bending, and driving.
  • Junior-level candidates must have a bachelor's, master's, or PhD degree with 0–3 years of related experience, including exposure or coursework in machine learning concepts and frameworks; alternatively, an associate's degree with 3 years of related experience, or a high school diploma/GED with 6 years of related experience.
  • Junior-level candidates must know machine learning frameworks and how to deploy ML models to production.
  • Mid-level candidates must have a bachelor's degree with 5 years of related experience, a master's degree with 3 years, an associate's degree with 8 years, or a high school diploma/GED with 11 years of related experience.
  • Mid-level candidates must have experience with machine learning frameworks and deploying ML models to production.
  • Senior-level candidates must have a bachelor's degree with 8 years of related experience, a master's degree with 6 years, an associate's degree with 11 years, or a high school diploma/GED with 14 years of related experience.
  • Senior-level candidates must have experience with machine learning frameworks and deploying ML models to production.

Responsibilities

  • Design, develop, and containerize machine learning models using PyTorch, Ray, Docker, and FastAPI.
  • Deploy, manage, and scale production ML workloads on Kubernetes.
  • Integrate AI/ML capabilities into full-stack applications using Python-based backend services and JavaScript frontend technologies.
  • Ensure model reliability, performance, and maintainability throughout the deployment lifecycle.
  • Architect and implement cloud-native ML infrastructure on AWS.
  • Develop and maintain DevOps and MLOps pipelines to streamline model development, testing, deployment, and monitoring.
  • Deploy and support AI/ML systems within secure, classified, and high-side environments.
  • Evaluate and integrate state-of-the-art AI/ML models, frameworks, and emerging technologies to enhance mission capabilities and accelerate innovation.
  • Architect scalable, resilient, and secure infrastructure to support evolving AI/ML workloads, production deployments, and mission-critical requirements.
  • Establish and champion best practices for production-grade machine learning systems, including MLOps, security, observability, and governance.
  • Provide technical guidance and thought leadership across AI/ML initiatives and engineering teams.

Desired Qualifications

  • Full-stack software development experience using Python and JavaScript.
  • Expert-level proficiency in Python with extensive experience across machine learning frameworks, including TensorFlow, PyTorch, and scikit-learn.
  • Ability to design and implement end-to-end machine learning pipelines covering data ingestion, feature engineering, model training, evaluation, deployment, and monitoring.
  • Experience with large language models, including fine-tuning, prompt engineering, retrieval-augmented generation, agentic workflows, and responsible AI practices.
  • Expertise in machine learning techniques including deep learning, reinforcement learning, generative models, ensemble methods, and modern model optimization approaches.
  • Experience designing and implementing production-grade MLOps infrastructure, including automated model retraining, monitoring, drift detection, and CI/CD pipelines using MLflow, Kubeflow, and SageMaker Pipelines.
  • Experience architecting and deploying scalable machine learning solutions on cloud platforms such as AWS SageMaker, Azure Machine Learning, and Google Vertex AI, with a focus on scalability, reliability, and cost optimization.
  • Experience leading technical architecture decisions and mentoring engineers on machine learning best practices, software engineering standards, experimentation, code quality, and research methodology.
  • Background in distributed computing and big data technologies such as Apache Spark, Ray, and Dask for efficient model training and inference.
  • Experience with containerization and orchestration technologies, including Docker and Kubernetes, to support scalable model serving, A/B testing, and canary releases or deployments.
  • Ability to translate complex business problems into well-scoped ML solutions and communicate trade-offs, risks, and return on investment to executive stakeholders.
  • Experience contributing to or publishing applied ML research, patents, conference presentations, or open-source projects.

About the Company

Noblis

Nonprofit federal contractor delivering systems engineering

Noblis is a nonprofit science, technology, and strategy organization that provides technical services and research to U.S. federal government agencies. It wins federal contracts and grants to deliver services such as systems engineering, program management, data analytics, cybersecurity, AI, and digital transformation across defense, homeland security, intelligence, and civil agencies. It differentiates itself by applying applied sciences and model-based engineering across multi-cloud environments and by partnering with small businesses to fulfill contracts, with research leading to patented technologies like DNA data storage and secure authentication. Its goal is to advance public-interest technology solutions through rigorous research and mission-focused programs that improve national security, public safety, and government operations.

Company size
1,001-5,000
Company stage
Not listed
Total funding
Not listed
Headquarters
Falls Church, VA
Founded
1996

Recently Posted Jobs

Explore more opportunities at Noblis

See more jobs