Machine Learning Engineer
NT Concepts is hiring a Machine Learning Engineer (active TS/SCI required) in Chantilly/Vienna, VA to take computer vision models from research prototypes to production for national security missions — building MLOps pipelines, training deep learning models in Python/PyTorch, and deploying containerized services in secure cloud environments.
- Compensation
- $120.3K–$180.5K / yr
- Location
- Chantilly, VA
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Contract
- Experience
- Not specified
- Category
- Ml / AI
- Posted
Skills
Must have
- Active TS/SCI clearance
- Professional experience developing, testing, and deploying ML models into production environments
- Strong Python programming skills
- Hands-on experience with deep learning frameworks (PyTorch, OpenCV, TensorFlow, or NumPy)
- Familiarity with containerization (Docker, Kubernetes)
- Familiarity with ML lifecycle/pipeline platforms (e.g., MLflow, Kubeflow, AWS SageMaker)
- Familiarity with cloud environments (AWS, Azure, or GCP)
- Modern development practices: Git, CI/CD pipelines, Agile methodologies
- Ability to understand mission objectives, iterate on user feedback, and communicate technical approaches
Nice to have
- CI Polygraph preferred or must be eligible to obtain
- Experience in secure, air-gapped, or classified cloud environments (e.g., AWS GovCloud / C2S)
- Experience with synthetic data generation or multi-modal models
- Exposure to LLMs or generative AI workflows
- Familiarity with distributed model training and GPU resource management
Full description
We are seeking a Machine Learning Engineer with a passion for building mission-critical capabilities to join our talent network. Working at NT Concepts means that you are part of an innovative, agile company dedicated to solving the most critical challenges in National Security. If meaningful work, initiative, creativity, and continuous self-improvement are important to your career, explore What's Next with us.
Mission Focus: Our machine learning teams bridge the gap between cutting-edge AI research and operational government missions. We are looking for engineers who can take machine learning and Computer Vision (CV) solutions from early research and prototyping all the way into stable, scalable production environments.
In this role, you will help design, build, and deploy automated ML workflows that directly support national security analysts and operators. We embrace modern agile practices, a DataOps/DevSecOps/MLOps ethos to "automate-first," and modern cloud-native architectures.
Clearance: Active TS/SCI required (CI Polygraph or higher preferred or must be eligible to obtain)
Location/Flexibility: Vienna, VA / Chantilly, VA (Hybrid / Flexible Remote options available)
Responsibilities
- Prototype to Production: Support the full machine learning lifecycle, taking computer vision models from experimentation and notebooks into containerized, high-throughput production microservices.
- Mission Alignment: Work closely with mission partners, domain experts, and technical teams to understand real-world operational challenges and translate them into practical ML requirements.
- MLOps & Pipeline Automation: Build, maintain, and optimize robust pipelines for data preparation, model training, validation, versioning, deployment, and monitoring using modern tools (such as MLflow, Kubeflow, and GitLab CI/CD).
- Model Development & Tuning: Train, fine-tune, and evaluate deep learning algorithms for computer vision tasks (e.g., object detection, classification, segmentation, tracking).
- System Integration: Collaborate with cross-functional software engineers and cloud architects to integrate ML models cleanly into larger enterprise systems and secure cloud infrastructures.
- Optimization & Governance: Optimize inference performance, apply secure coding practices, and monitor models for drift and reliability once deployed.
Qualifications
- Clearance: Active TS/SCI clearance.
- Hands-On Experience: Demonstrated professional experience developing, testing, and deploying machine learning models into real-world or production environments.