R&D Engineer

Responsibilities

• Research, design, and build novel neural network architectures for next-generation YOLO models
• Develop core components including backbones, necks, heads, attention modules, and efficient building blocks
• Read, reproduce, and extend cutting-edge papers into working model architectures from scratch
• Run rigorous ablation studies and large-scale architecture experiments across standard benchmarks
• Improve accuracy, latency, and scaling through distillation, pruning, and quantization-aware design
• Build training recipes and evaluation pipelines for detection, segmentation, pose, and new vision tasks
• Partner with R&D and engineering teams to turn research ideas into production-ready open-source models

Requirements

• 5+ years of hands-on experience in computer vision and deep learning with architecture design expertise
• Proven success building models from the ground up
• Expert-level Python and deep proficiency in PyTorch
• Strong command of convolutions, attention, normalization, optimization, and loss design fundamentals
• Ability to implement research papers faithfully and efficiently from scratch
• Experience with efficiency research such as quantization, pruning, distillation, or neural architecture search
• Strong portfolio of research contributions (papers, preprints, or open-source code)

Preferred Qualifications

• Published research at venues such as CVPR, ICCV, ECCV, NeurIPS, or ICLR
• Experience with distributed training, mixed precision, CUDA profiling, or model deployment constraints
• Active open-source presence with architecture-focused repositories
• Familiarity with the Ultralytics Python package and documentation

Benefits

• Competitive salary and equity packages
• Flexible working hours
• 24 vacation days plus birthday off and local holidays
• Brand-new Apple hardware (MacBook, Studio Display, AirPods Pro)
• Dedicated budget for learning and development

About the Company

Ultralytics is the creator of the world's leading open-source YOLO models, empowering millions of developers to build state-of-the-art computer vision applications.