United States onlyUnstructured is seeking an AI/Machine Learning Engineer to join our Public Sector team in support of US government clients, primarily across the Department of Defense and broader national security community. This is a high-profile role that requires deep technical expertise and customer engagement skills to deliver complex software implementations on government networks. The ideal candidate will have strong hands-on technical abilities and substantial experience building and deploying AI models or AI applications that meet the high technical and security standards of the US government.
This role will involve testing, evaluation, and development of various models and implementation architectures for use on US government networks. Machine learning engineers with a background in Large Language Models (LLMs), as well as those with a foundation in computer vision, autonomy, sensor fusion, or core defense technologies, such as signals, electronic warfare, or cyber, are encouraged to apply.
Unstructured deeply values past government and military service and welcomes veterans of the US military.
TS Active Clearance required for the role + ability to travel.
Location: Fully Remote with occasional travel to North Carolina, Florida, and other CONUS locations
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field. Master’s or PhD a plus.
4+ years of experience in AI/ML engineering, MLOPS, systems architecture, or similar technical roles
2+ years of experience working with government networks and security requirements
An understanding of government security frameworks (FedRAMP, NIST 800-53, FISMA, DISA SRG) and how they apply to ML workloads
History of leading or delivering high-impact ML initiatives in enterprise or government environments; preference for those with articulable experience assessing performance of alternative models, architectures, and implementation strategies
A commitment to meeting the demanding engineering standards required to support national security and defense clients
A strong interest in being at the forefront of the AI revolution
Key Responsibilities
Develop evaluation and assessment tools and frameworks to measure newly developed models for performance against key metrics across a wide domain of tasks and knowledge sets
Identify, propose, and implement modifications of existing models and model implementation frameworks to optimize for new tasks
Lead conceptualization of both traditional and agentic implementation strategies for cloud and on-premises model deployments within broader system architectures
Lead and optimize distributed ML workloads on multiple government cloud and non-cloud infrastructures..
Align AI/ML deployments with FedRAMP, NIST 800-53, FISMA, and DISA SRG, maintaining strict security standards.
Create reference architectures and deployment patterns to streamline ML adoption across government agencies.
Translate mission objectives into ML-focused technical specifications and project plans.
Apply advanced security controls and zero-trust architectures to protect ML pipelines and data.
Continuously assess ML workloads for performance, cost, and security improvements, driving ongoing refinement.
Required Technical Skills
Cloud Platforms
Familiar with AWS, Azure, and/or GCP services for ML workloads
Experience with government cloud offerings (AWS GovCloud, Azure Government, etc.)
Multi-cloud ML architecture design and implementation
Cloud cost optimization and resource governance for AI/ML
Familiarity with:
Knowledge of Kubernetes administration (EKS, AKS, GKE)
Container security and compliance for ML containers
Experience with IaaC, such as Terraform, Ansible, Pulumi, etc., for provisioning complex ML environments
CI/CD pipeline integration for automated ML model deployment
Security
Network security for ML pipelines
Government compliance frameworks
Security automation and continuous compliance monitoring
Programming & Development
Python proficiency (ML model development, data processing, pipeline orchestration)
API design and development for ML services
Debugging and performance optimization in ML systems
Code review and quality assessment
Core Competencies
Strategic thinking and architectural vision for AI/ML initiatives
Executive-level communication skills, especially when conveying complex ML concepts
Technical innovation and team leadership in ML/AI settings
Problem-solving in high-pressure mission-critical environments
Stakeholder management across technical and non-technical audiences
Why Unstructured
Shape the security roadmap of a company at the forefront of AI and data infrastructure.
Collaborate with world-class engineers and leaders on mission-critical initiatives.
Competitive compensation, equity, and benefits
CA and US only
