Motion Recruitment | Jobspring | Workbridge

Applied Machine Learning Engineer // AI // Remote

Alexandria, Virginia

100% Remote

Full Time

$150k - $200k

The leader in maritime domain awareness is building advanced sensing and detection systems for naval and commercial vessels worldwide. From ruggedized HD camera arrays to AI-powered analytics, this technology enables crews to see more, respond faster, and operate safer at sea. They’re looking for a full-time Machine Learning Engineer who will be working directly with cutting-edge locally deployed systems built in Python, Node.js, PyTorch, and TensorFlow.

This role offers the opportunity to apply advanced machine learning techniques directly to mission-critical maritime systems. Instead of deploying to the cloud, you’ll work on models that run locally on ruggedized hardware to ensure performance, security, and reliability in challenging real-world conditions. They’re looking for an engineer with strong technical depth who values precision, efficiency, and collaboration. This position provides room to expand your expertise in computer vision and edge computing, while contributing to technology that has a direct impact on safety and operational effectiveness at sea.

Required Skills & Experience
  • Strong proficiency in Python
  • Hands-on experience with PyTorch and/or TensorFlow
  • Background in Machine learning model development and optimization
Desired Skills & Experience
  • Exposure to edge computing or locally deployed/embedded systems
  • Experience design experiments and evaluating model performance
  • Familiarity with signal processing or maritime/defense applications
What You Will Be Doing
Tech Breakdown
  • 50% Python
  • 25% Node.js
  • 15% TypeScript
  • 10% NoSQL/MongoDB

The Offer
  • $150-$200k/year
  • Bonus eligible
You will receive the following benefits:
  • Medical, Dental, and Vision Insurance
  • Vacation Time
  • Stock Options

Applicants must be currently authorized to work in the US on a full-time basis now and in the future.

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Posted by: Charles Deuter

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