Machine Learning Engineer (Detect & Track Distillation)

The role

You will take large foundation models and distill them into highly optimized, task-specific components. Your work focuses on target detection, classification, and target re-identification across time for diverse edge and embedded systems.

What you will do

• Compress and distill large foundation models into efficient components for target detection.
• Optimize neural networks for constrained embedded systems using quantization, pruning, and LoRA.
• Build, modify, and manage training, evaluation, and MLOps pipelines.
• Collaborate on data curation and the creation of task-specific datasets.
• Perform framework-level benchmarking of distilled models for performance and latency.
• Research scientific trends in computer vision and quantization research.
• Work with Detect&Track Foundation team, System Engineers, Project Teams, and Mission Intelligence.
• Support the team by managing or mentoring junior engineers depending on seniority.

Required qualifications

• Degree in a STEM field (e.g., Computer Science, Engineering, Mathematics).
• Experience running vision neural networks, developing target detection architectures, or managing re-identification tasks.
• Expertise in knowledge distillation, model compression, and deploying networks onto constrained embedded systems or edge hardware.
• Proficiency in MLOps, GPU compute, and building infrastructure.
• Highly structured, analytical, task-aligned, and research-oriented.
• Excellent communication and influence skills.
• Ability to thrive under pressure in a fast-paced environment.
• 100% dedication to Harmattan AI’s mission, vision, and ambitious growth plans.

About the company

Harmattan AI is a next-generation defense prime building autonomous and scalable defense systems. The company is expanding its teams and capabilities to deliver mission-critical systems to allied forces.