Location: Seattle, WA / Sterling, VA
Job Type: Full-Time | Hybrid | Mondays Remote, Tuesday – Friday Onsite
Target Salary Range*: $215,000 - $230,000 ($400,000 - $450,000 OTE)

*This represents the potential salary range for this position depending on education level, years of experience and/or certifications in addition to other position specific requirements which may impact salary

Position Overview

The Principal Machine Learning Engineer contributes to the architecture and implementation of the platform used by scientists to bring safe, secure, and reliable AI capabilities to counter-UAS devices. Working closely with scientists and software engineers, this role enables new AI functionality across areas such as multimodal sensor fusion, real-time systems, and distributed edge computing.

The Principal Machine Learning Engineer supports the full AI lifecycle, from research and model development through optimization and deployment into production environments. This role focuses heavily on deploying and managing AI models at the edge, including resource-constrained devices with limited memory and connectivity, while ensuring models are performant, reliable, and suitable for real-world use.

Key Responsibilities

Machine Learning and Deep Learning

• Develop, own, optimize, and deploy machine learning and deep learning models into production software products.
• Support scientists throughout the AI lifecycle, from research and experimentation through production deployment.
• Translate research outputs into scalable and reliable production AI capabilities.
• Contribute to the development of safe, secure, and responsible AI solutions.

Computer Vision, Signal Processing, and Sensor Fusion

• Develop and support machine learning solutions involving computer vision, signal processing, and sensor fusion.
• Integrate multiple sensor data sources using advanced machine learning techniques.
• Support multimodal AI capabilities used in real-time and distributed systems.
• Apply machine learning approaches to complex sensing and perception problems.

Edge AI and Model Optimization

• Deploy and optimize machine learning models on AI chips and edge devices.
• Optimize models for environments with limited memory, compute, and connectivity.
• Support on-device model deployment, management, and lifecycle activities.
• Improve model performance and efficiency for real-time edge applications.

ML Infrastructure and Platform Engineering

• Architect and maintain large-scale distributed machine learning platforms and systems.
• Support infrastructure used by scientists and engineers to develop, test, deploy, and operate AI capabilities.
• Evaluate system architecture, performance metrics, code, test plans, project plans, deployments, and operational requirements.
• Support reliable deployment of AI capabilities across distributed edge-compute environments.

Software Engineering and Production Deployment

• Develop production software using Python and C++.
• Use machine learning frameworks such as PyTorch and TensorFlow.
• Take machine learning models from research and development through deployment into live software products used by customers.
• Collaborate with software engineers to integrate AI models into production applications and systems.

Technical Leadership and Collaboration

• Provide technical leadership across machine learning, edge AI, and ML infrastructure initiatives.
• Collaborate closely with scientists, engineers, and product managers.
• Contribute to architectural decisions and technical direction for AI-enabled systems.
• Support multidisciplinary teams operating across Public Safety, Enterprise, and Defense domains.

Qualifications

Education

• B.S. in Engineering, Mathematics, Physics, or another highly technical field. [Required]

Experience

• 13+ years of software engineering experience. [Required]
• Proven experience architecting and maintaining large-scale distributed platforms. [Required]
• Proven experience developing, owning, and deploying machine learning models into live production products. [Required]
• Experience deploying and optimizing AI models on chips and edge devices. [Required]
• Experience with computer vision, signal processing, and machine learning infrastructure. [Required]
• Experience taking machine learning models from research and development through deployment into production software used by customers. [Required]

Skills

• Strong Python and C++ proficiency. [Required]
• Experience with machine learning frameworks such as PyTorch and/or TensorFlow. [Required]
• Advanced knowledge and hands-on experience with on-chip AI development. [Required]
• Strong understanding of edge model deployment and management. [Required]
• Excellent problem-solving skills. [Required]
• Ability to analyze system architecture, design, performance metrics, code, test plans, project plans, deployments, and operations. [Required]
• Ability to communicate and collaborate effectively with scientists, engineers, and product managers. [Required]

Preferred Qualifications

• Master’s degree or PhD in Computer Science, Engineering, Physics, Mathematics, or an equivalent highly technical field.
• Hands-on experience solving computer vision or natural language understanding problems in a business environment.
• Familiarity with responsible AI, de-biasing techniques, model encryption, and de-identification techniques.