Machine Learning Engineer (Detect & Track Distillation)

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).