R&D Engineer

Accountabilities:

• Research, design, and develop novel neural network architectures for next-generation YOLO models.
• Build and improve core model components, including backbones, necks, heads, attention mechanisms, and efficient architectural building blocks.
• Read, reproduce, adapt, and extend cutting-edge research papers into working architectures from scratch.
• Design and execute rigorous ablation studies and large-scale experiments using relevant computer vision benchmarks.
• Improve model accuracy, latency, and scalability through techniques such as knowledge distillation, pruning, and quantization-aware design.
• Develop training strategies and evaluation pipelines for detection, segmentation, pose estimation, and emerging vision tasks.
• Work closely with R&D and engineering teams to transform research concepts into production-ready open-source models.
• Contribute to foundational model initiatives and future computer vision systems while taking ownership of assigned R&D challenges and delivering solutions efficiently.

Requirements:

• 5+ years of hands-on experience in computer vision and deep learning, with strong expertise in neural network architecture design.
• Demonstrated ability to build deep learning models from the ground up rather than primarily fine-tuning pretrained checkpoints.
• Expert-level Python skills and deep proficiency with PyTorch, including custom layers, modules, and training loops.
• Strong understanding of convolutions, attention mechanisms, normalization, optimization, and loss-function fundamentals.
• Ability to read research papers and implement proposed methods accurately and efficiently from scratch.
• Experience with model-efficiency techniques such as quantization, pruning, knowledge distillation, or neural architecture search.
• Strong research portfolio demonstrated through papers, preprints, open-source contributions, or original model development.
• High ownership, adaptability, and comfort working in a fast-paced, research-driven environment.
• Published research at conferences such as CVPR, ICCV, ECCV, NeurIPS, or ICLR is a plus.
• Experience with distributed training, mixed-precision training, CUDA profiling, or deployment constraints is advantageous.
• Active open-source involvement, particularly with architecture-focused projects and meaningful community adoption, is a plus.
• Experience taking original research from an initial prototype through to models that are adopted and used in practice is highly valued.

Benefits:

• Competitive salary based on experience and contribution.
• Equity package designed to share in the organization's success.
• Fully remote working environment with flexible working hours.
• 24 vacation days, an additional day off for your birthday, and local public holidays.
• Opportunity to work on cutting-edge AI and computer vision technology with broad real-world impact.
• Brand-new Apple MacBook Air or MacBook Pro, Apple Studio Display, and AirPods Pro 3.
• Dedicated budget for personal and professional learning and development.
• Collaboration with a global team of AI researchers, engineers, and builders.
• Opportunity to contribute to technology used across millions of devices and applications.
• A high-impact environment focused on innovation, technical excellence, ownership, and continuous improvement.