Invest in your career with a Madrona-funded company.

Unstructured 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.

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

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.

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

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

Network security for ML pipelines

Government compliance frameworks

Security automation and continuous compliance monitoring

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

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

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