Staff Machine Learning Scientist/Engineer (Embodied AI)

Location: Sunnyvale, California, USA; hybrid work with time in offices and workshops and time working from home

Salary: $370,000–$419,000 per year, plus equity package

Company

hirify.global Labs develops foundation models and embodied intelligence systems for general-purpose robots.

What you will do

• Research and develop model architectures, learning objectives, and data strategies for robot foundation models.
• Build self-supervised and generative pre-training methods using web video, egocentric video, and robot-interaction data.
• Develop post-training methods including supervised fine-tuning, imitation learning, and reinforcement learning.
• Curate, filter, and evaluate large-scale robotics datasets, including egocentric, UMI, and teleoperated data.
• Build and use distributed training pipelines for large models and multimodal datasets.
• Collaborate with research, engineering, robotics, and hardware teams to connect model progress to real-world robot performance.

Requirements

• Experience in machine learning, with a focus on multimodal foundation models and foundation-model data.
• Experience with scalable training, multi-node training, large datasets, or large model training.
• Strong research track record, including publications in top-tier venues such as ICRA, CoRL, CVPR, NeurIPS, ICML, or ICLR.
• Strong engineering skills and hands-on experience with modern machine learning frameworks.
• Ability to design rigorous experiments and translate research ideas into working systems, experiments, or deployed capabilities.
• Strong communication skills and ability to collaborate across engineering and robotics teams.

Nice to have

• PhD or MS in Computer Science, Machine Learning, Robotics, Computer Vision, or a related technical field.
• Industry experience in machine learning, robotics, embodied AI, or applied research.
• Experience with real robots, robotic learning, simulation, or policy learning.
• Experience with large-scale video data and sequential decision-making systems.

Culture & Benefits

• Hybrid working policy combining office and workshop collaboration with work from home.
• Core working hours with flexibility to determine a suitable schedule.
• Opportunity to work on a new ML-first research program and evaluate models on a growing fleet of robots.
• Competitive equity package.