Maven Robotics, Inc.
Machine Learning Engineer - Foundation Models
Maven Robotics, Inc., San Francisco, California, United States, 94199
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.
Responsibilities
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.
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AI Foundation Model Engineer
to design, implement, test, and deploy state-of-the-art foundation models on our robots.
Responsibilities
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.
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