Machine Learning Engineer - Foundation Models

Company Overview

Maven Robotics is building the world’s leading general-purpose AI robots.

We are currently operating in stealth and are growing the world’s best team in AI robotics. We are looking for self-starters that are the world’s best in their field, who can innovate from a deep understanding of the fundamentals, and who share our values of unwavering truth seeking and integrity, humility, curiosity, and relentless determination.

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 pre-training, 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