About the job
About Us
At Harmattan AI, we are pioneering the development of autonomous and scalable defense systems for the next generation. Following a successful $200M Series B funding round that has valued our company at $1.4 billion, we are on an exciting growth trajectory, enhancing our teams and technological capabilities to deliver mission-critical systems to allied forces.
Our operations are driven by a commitment to creating technologies that make a significant real-world impact, striving for excellence in all endeavors, setting lofty goals, and tackling the toughest technical challenges. We thrive in a high-stakes environment where rigor, ownership, and execution are paramount.
Responsibilities
Develop and implement deep learning models targeted at computer vision applications.
Engage in research and experimentation utilizing Convolutional Neural Networks (CNNs) and Vision Transformers.
Apply model compression techniques, including knowledge distillation.
Conduct quantization-aware training (QAT) and post-training quantization (PTQ).
Explore network and dataset pruning strategies for optimization.
Design efficient architectures suitable for edge and embedded systems.
Oversee dataset curation, balancing, and bias mitigation processes.
Conduct experimental design, ablation studies, and ensure reproducibility of practices.
Perform robust evaluations using relevant metrics (e.g., mAP, IoU, calibration).
Analyze failure cases and conduct robustness testing under distribution shifts.
You will be encouraged to read, analyze, and implement insights from top-tier conferences such as CVPR, ICCV, ICLR, and NeurIPS.
