Robotic AI Engineer/Applied Scientist - Foundation Models

Robotic AI Engineer/Applied Scientist - Foundation Models

LOCATION:

Role Description

We are seeking an exceptional AI Foundation Model Engineer to design, implement, test, and deploy state-of-the-art foundation models on our robots.

In this role, you will:

• Develop, fine-tune, and optimize foundation models for the robot, leveraging advanced techniques such as pretraining, fine-tuning, and retrieval-augmented generation (RAG).
• Translate high-level application goals into foundational AI solutions, delivering robust and scalable models that perform effectively in real-world scenarios.
• Build pipelines for training, evaluation, and deployment of models on various platforms, ensuring high performance and adaptability across diverse use cases.
• Stay at the forefront of research, incorporating cutting-edge advancements in AI and foundation models to enhance system capabilities and push technical boundaries.
• Own critical projects end-to-end, from model design and experimentation to deployment and continuous improvement.
• Collaborate with cross-functional teams, including data, software, and systems engineers, to integrate models seamlessly into production workflows.

Qualifications

Must-have:

• MS or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• Deep understanding of machine learning concepts, including attention mechanisms, transformers, and training techniques
• Experience working with fine-tuning, retrieval-augmented generation (RAG), prompt engineering, or reinforcement learning with human feedback (RLHF).
• Proficiency in programming languages and tools commonly used in machine learning (e.g. Python).
• A strong grasp of theoretical concepts and practical implementation of multimodal foundation models.

Nice-to-have:

• Experience with deployment of models to edge devices for real-time inference.
• Experience with large-scale distributed training and optimization frameworks (e.g. PyTorch, TensorFlow) and familiarity with tools for model compression and quantization
• General knowledge of robotics principles, including kinematics, dynamics, and control.
• Publications or contributions to the machine learning community, particularly in areas related to robotics or large language models.
• Excellent problem-solving skills, with the ability to innovate and experiment with novel approaches