Principal Machine Learning Engineer

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