Amazon Applied Scientist II FAR Frontier AI & Robotics 

United States, Washington, Seattle 

670638182

Key job responsibilities
- Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish
- Drive independent research initiatives in robotics foundation models, focusing on breakthrough approaches in perception, and manipulation, for example open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization
- Lead technical projects from conceptualization through deployment, ensuring robust performance in production environmentsA day in the life
- Design and implement novel foundation model architectures, leveraging our extensive compute infrastructure to train and evaluate at scale- Lead technical initiatives from conception to deployment, working closely with robotics engineers to integrate your solutions into production systems- Leverage our massive compute cluster and extensive robotics infrastructure to rapidly prototype and validate new ideas
- Transform theoretical insights into practical solutions that can handle the complexities of real-world robotics applications
1. Medical, Dental, and Vision Coverage
2. Maternity and Parental Leave Options
3. Paid Time Off (PTO)
4. 401(k) Plan

- PhD in computer science, machine learning, engineering, or related fields
- Experience programming in Java, C++, Python or related language
- Publication record at top-tier conferences (NeurIPS, ICML, ICLR, CVPR, ICCV, RSS, CoRL)
- Experience developing and implementing deep learning models
- Strong programming skills in Python and PyTorch/JAX
- Track record of solving complex technical problems

- Experience using Unix/Linux
- First-author publications at major ML/robotics conferences
- Experience with foundation models or large language models
- Background in computer vision, robotics, or related fields
- Experience with sim2real transfer or multi-task learning
- Familiarity with distributed training systems
- Track record of deploying ML models in production environments
- Experience with large-scale machine learning systemsPursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.