Machine Learning Research Scientist and Engineer

Reward AI; San Francisco (CA); On-site; USD 140,000 - 230,000; Full time

Job summary

Reward AI is seeking researchers and engineers to advance models, data, and systems at the intersection of machine learning, robotics, and hardware. You will work on foundation models, multimodal learning, and data pipelines, with opportunities across LLMs, VLMs, and robot learning.

Prior robotics experience is welcome but not required. You will collaborate with researchers and robotics engineers to evaluate models in simulation and on real robots, driving practical AI capabilities and scalable

Qualifications

• Foundation in machine learning, computer science, data engineering, mathematics, or related area.
• Experience in one or more of LLMs, VLMs, foundation models, multimodal learning, computer vision, imitation learning, reinforcement learning, robot learning, or data engineering.
• Depth in any one area is valuable; experience across all of them is not expected.
• Strong programming skills and experience building models, data pipelines, or scalable software systems.
• Evidence of thoughtful research or strong engineering through projects, publications, open-source contributions, or deployed systems.

Responsibilities

• Contribute across model research, data engineering, or ML systems, depending on strengths and interests.
• Develop and improve foundation models through pretraining, fine-tuning, post-training, or new approaches to learning and reasoning.
• Explore robot learning methods such as imitation learning, behavior cloning, reinforcement learning, and learning from human video to develop generalizable manipulation and whole-body skills.
• Build scalable pipelines for collecting, processing, curating, and serving diverse datasets, including text, images, video, and other modalities.
• Design experiments and evaluations, analyze model behavior and data quality, and improve training or inference efficiency and reliability.
• Collaborate with researchers and robotics engineers to evaluate models in simulation and on real robots, translate advances in general AI into useful capabilities, and shape technical priorities.

Skills

Foundation in ML / CS

Strong programming

Curiosity & communication

Education

Bachelor's degree in a related field

Tools

Python

PyTorch

SQL

JAX

Job description

At Reward AI, we are building frontier intelligence for any robot. Our work brings together machine learning, robotics, and hardware to help robots learn from people and perform useful work across bodies, tasks, and environments. We welcome researchers, engineers, and people with experience in operations and product from a wide range of backgrounds.

Advance the models, data, and systems that help AI understand the world, reason, and act. This role spans foundational research and practical engineering, with opportunities across large language models (LLMs), vision-language models (VLMs), multimodal learning, foundation models, data engineering, and robot learning. Robotics-focused work includes imitation learning from human demonstrations, reinforcement learning, and policies that generalize across tasks and robot bodies. You could develop new modeling approaches, build data pipelines, scale training and inference, or create better ways to evaluate and improve models. Your work will contribute to Reward AI’s mission of building general robot intelligence, with a scope shaped around your strengths. Prior robotics experience is welcome but not required.

Responsibilities

• Contribute across model research, data engineering, or ML systems, depending on your strengths and interests.
• Develop and improve foundation models through pretraining, fine-tuning, post-training, or new approaches to learning and reasoning.
• Explore robot learning methods such as imitation learning, behavior cloning, reinforcement learning, and learning from human video to develop generalizable manipulation and whole-body skills.
• Build scalable pipelines for collecting, processing, curating, and serving diverse datasets, including text, images, video, and other modalities.
• Design experiments and evaluations, analyze model behavior and data quality, and improve training or inference efficiency and reliability.
• Collaborate with researchers and robotics engineers to evaluate models in simulation and on real robots, translate advances in general AI into useful capabilities, and shape technical priorities.

Preferred qualifications

• A foundation in machine learning, computer science, data engineering, mathematics, or a related area, developed through study or practical work.
• Experience in one or more of LLMs, VLMs, foundation models, multimodal learning, computer vision, imitation learning, reinforcement learning, robot learning, or data engineering. Depth in any one area is valuable; experience across all of them is not expected.
• Strong programming skills and experience building models, data pipelines, or scalable software systems. Relevant tools may include Python, SQL, PyTorch, JAX, or distributed data processing frameworks.
• Evidence of thoughtful research or strong engineering through projects, publications, open-source contributions, or deployed systems.
• Curiosity, clear communication, and the ability to turn open-ended questions into concrete experiments.