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Paris, Ile-de-France, France • On-site
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The role focuses on distilling large foundation models into optimized, task-specific components for target detection and classification on embedded platforms. Responsibilities include managing MLOps pipelines, optimizing networks for edge hardware, and collaborating cross-functionally to deliver robust autonomous defense solutions.
Candidates need a STEM degree and proven experience in deep learning, computer vision, and model compression for constrained hardware. Proficiency in MLOps and the ability to thrive in a fast-paced, research-oriented environment are essential.
Harmattan AI is a next-generation defense prime building autonomous and scalable defense systems. Following the close of a $200M Series B, valuing the company at $1.4 billion, we are expanding our teams and capabilities to deliver mission-critical systems to allied forces.
Our work is guided by clear values: building technologies with real-world impact, pursuing excellence in everything we do, setting ambitious goals, and taking on the hardest technical challenges. We operate in a demanding environment where rigor, ownership, and execution are expected.
Harmattan AI is heavily pushing the boundaries of autonomous systems, where the perception of the surrounding world through visual cues is a vital component. To make sense of incoming visual data and enable mission-critical downstream decisions, we have developed custom detection models. As our product and project portfolio expands, we are diversifying our efforts in this space across multiple embedded platforms.
As an ML Research Engineer in the Detect&Track Distillation team, you will join us at a very early stage, giving you a unique opportunity to heavily influence the technical direction of the team. Operating out of Lausanne, Paris, or Zurich, you will focus on taking large foundation models and distilling them into highly optimized, task-specific components. Your work will span target detection, classification, and target re-identification across time, directly tackling the hardware inference constraints of diverse edge and embedded systems.
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.
Educational Background: A strong academic record with a degree in a STEM field (e.g., Computer Science, Engineering, Mathematics).
Deep Learning & Computer Vision: Proven experience running vision neural networks, developing target detection architectures, or managing re-identification tasks.
Model Compression & Edge AI: 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).
Technical Competence & Infrastructure: Proficiency in MLOps, GPU compute, and building infrastructure (such as training pipeline templates and loggers).
Professional Attributes:
Highly structured, analytical, task-aligned, and research-oriented.
Excellent communication and influence skills, with the ability to effectively translate and present complex benchmarking data to downstream users and senior stakeholders.
Thrives under pressure in a fast-paced environment with a “no-task-is-too-small” mentality toward building foundational team infrastructure.
Commitment: 100% dedication to Harmattan AI’s mission, vision, and ambitious growth plans, ready to go the extra mile to ensure operational excellence
We look forward to hearing how you can help shape the future of autonomous defense systems at Harmattan AI.
Building Autonomous Forces. Coordinated Saturation at the Edge.
Harmattan AI is redefining how forces are built and deployed, shaping a future where autonomous robots fight as one across the full spectrum of operations. Autonomy multiplies both mass and capability, unlocking a new scale of force. Coordinated saturation is the doctrine of modern warfare, and advantage shifts from the platform to the autonomous force.
Offices: 1, Rue du Mail, Paris, Île-de-France 75002, FR
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