ML Research Engineer (Detect & Track Distillation)

Posted Yesterday
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Paris, Île-de-France, FRA
In-Office
Mid level
Artificial Intelligence • Computer Vision • Machine Learning • Robotics • Defense • Manufacturing
Building the Future of Autonomous Warfare. With Speed and Intelligence.
The Role
Distill and compress large vision foundation models into efficient detectors and re-identification modules for constrained edge and embedded hardware. Optimize models (quantization, pruning, LoRA), build and maintain MLOps training/evaluation pipelines, curate datasets, benchmark latency/performance, and collaborate with systems and mission teams to deploy robust solutions.
Summary Generated by Built In
About Us

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.

About the Role

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.

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.

Candidate Requirements
  • 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.

Skills Required

  • Degree in a STEM field (Computer Science, Engineering, Mathematics)
  • Proven experience with vision neural networks, target detection architectures, or re-identification tasks
  • Hands-on expertise in knowledge distillation and model compression
  • Experience optimizing and deploying models to constrained embedded systems (e.g., Jetson, custom NPUs, wearables)
  • Practical experience with quantization (PTQ and QAT), pruning, and LoRA
  • Proficiency in MLOps, GPU compute, and building training/inference infrastructure (pipelines, logging, version control)
  • Strong communication and ability to present benchmarking results to stakeholders
  • Mentorship or management of junior engineers (depending on seniority)
  • Commitment to company mission and ability to work in high-pressure, fast-paced environment

Harmattan AI Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Harmattan AI and has not been reviewed or approved by Harmattan AI.

  • Fair & Transparent Compensation Pay ranges are publicly shown for multiple U.S. roles (e.g., $140k–$200k base) and appear broadly in line with late‑stage startup/defense‑tech expectations based on the postings cited.
  • Equity Value & Accessibility Equity is repeatedly referenced in several job postings as part of total compensation, which can increase upside potential at a recently funded, high‑valuation company.
  • Strong & Reliable Incentives Sign‑on bonuses are explicitly mentioned in a hiring post for candidates who can start quickly, indicating the use of cash incentives in at least some hiring situations.

Harmattan AI Insights

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The Company
HQ: Paris, Île-de-France
131 Employees

What We Do

Harmattan AI is rising as a next-generation defense prime, building the future of autonomous warfare. We leverage AI-driven autonomy, real-time intelligence, and conflict-ready production to deliver attritable systems and autonomous mission management software. Designed for the real-world needs of warfighters, our solutions enable faster deployment, sharper decision-making, and battlefield dominance.

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