Applied Scientist, Amazon Robotics

Jobverse.io; Sunnyvale (TX); On-site; USD 140,000 - 200,000; Full time

Job summary

Amazon Robotics is seeking an Applied Scientist to develop and evaluate robotics foundation models, including vision-language-action, reasoning, task-planning, and reinforcement-learning models, with collaboration across scientists and engineers to advance intelligent robotic systems.

Responsibilities include model development and training, data management, experimentation, code development, and cross-team collaboration to integrate with robotics hardware teams.

Qualifications

• PhD or equivalent research experience in ML/robotics.
• Experience with large-scale model training and data pipelines.
• Strong collaboration and publication/communication abilities.

Responsibilities

• Design and implement foundation model architectures; train and fine-tune with diverse datasets.
• Prepare data pipelines, governance, provenance tracking, and data quality checks.
• Design and run experiments to test model on simulators and embodied setups; compare baselines.
• Write clean, well-documented code; contribute to training infra and model evaluation tools.
• Stay current with foundation models and robotics; prepare reports and presentations.
• Collaborate with senior scientists/engineers; support hardware integration and share best practices.

Skills

Machine Learning

Robotics

Foundation Models

Python

Experimentation

Education

PhD in CS/ML/Robotics

Tools

PyTorch

TensorFlow

CUDA

Git

Job description

Amazon is a global technology company developing advanced robotics, machine learning, and artificial intelligence solutions for complex operational challenges. Amazon Robotics is seeking an Applied Scientist to develop and evaluate robotics foundation models, including vision-language-action, reasoning, task-planning, and reinforcement-learning models, while collaborating with scientists and engineers to advance intelligent robotic systems.

Responsibilities

• Model Development and Training: Designing and implementing the model architectures, training and fine tuning the foundation models using various datasets, and optimize the model performance through iterative experiments
• Data Management: Process and prepare training data, including data governance, provenance tracking, data quality checks and creating reusable data pipelines
• Experimentation and Validation: Design and execute experiments to test model capabilities on the simulator and on the embodiment, validate performance across different scenarios, create a baseline and iteratively improve model performance
• Code Development: Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs
• Research: Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions
• Collaboration: Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies