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Machine Learning Engineer (Detect & Track Distillation)

Harmattan AI· R&D
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Paris, Lausanne, ZurichFull-timePosted 3mo ago

About the role

ML Research Engineer focused on distilling large foundation models into optimized, task-specific components for target detection, classification, and target re-identification across time. Role spans edge AI optimization, data curation, MLOps pipelines, and collaboration with cross-functional teams to deliver robust, hardware-aware computer vision solutions for embedded systems.

Responsibilities

  • Model Distillation & Finetuning: Take large foundation models and compress/distill them into highly specific, efficient components optimized for smaller tasks and target detection.
  • Edge AI Optimization: Optimize neural networks for constrained embedded systems using techniques such as quantization (PTQ vs. QAT), pruning, and LoRA.
  • Pipeline Management & MLOps: Build, heavily modify, and manage training, evaluation, and MLOps pipelines while ensuring reproducibility, robust logging, and version control.
  • Data Curation: Collaborate on data curation and the creation of task-specific datasets to constantly improve model accuracy.
  • Benchmarking & Evaluation: Framework-level benchmarking of newly distilled models to evaluate performance and latency, ensuring results are fully aligned with real-world operational deployments.
  • Research & Innovation: Stay at the absolute forefront of scientific trends in computer vision and quantization research to introduce cutting-edge methodologies to the team.
  • Cross-Functional Collaboration: Work closely with the Detect&Track Foundation team, downstream System Engineers, Project Teams, and Mission Intelligence to deliver robust solutions.
  • Mentorship: Depending on seniority, support the team by managing or mentoring junior engineers.

Qualifications

  • A strong academic record with a degree in a STEM field (e.g., Computer Science, Engineering, Mathematics).
  • Proven experience running vision neural networks, developing target detection architectures, or managing re-identification tasks.
  • Hands-on expertise in knowledge distillation, model compression, and deploying networks onto highly constrained embedded systems or edge hardware (e.g., Jetson, custom NPUs, wearables).
  • Proficiency in MLOps, GPU compute, and building infrastructure (such as training pipeline templates and loggers).
  • Highly structured, analytical, task-aligned, and research-oriented.
  • Excellent communication and influence skills, with the ability to translate benchmarking data to downstream users and senior stakeholders.
  • Thrives under pressure in a fast-paced environment with a proactive mindset toward building foundational team infrastructure.
  • Commitment to Harmattan AI’s mission and growth plans.

Skills

Required:Distillation and FinetuningEdge AI and DeploymentMLOps & Infrastructure
Last updated Sep 23, 2026. Interested? Apply on the company site.
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Building the Future of Autonomous Warfare.

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