Founding Research Scientist

Company Overview

Verne is building physical AI to make physical work as scalable as software and bring general-purpose robots to every business. We develop robots, foundation models, and the tools that turn them into reliable industrial deployments.

Our open-weight world action model, Verne V0.5 , brings together video and action learning. Alongside it, we build data and training systems, a fine-tuning platform, and inference and control software. Our work is grounded in real deployments: learning new tasks efficiently, adapting to different environments, and operating reliably in production.

The Role

We’re looking for a Founding Research Scientist to shape our robot learning research and turn promising methods into reliable manipulation skills. You’ll own projects from the initial hypothesis through model training, evaluation, and experiments on physical robots, working closely with engineering.

Key Responsibilities

• Develop world action models and robot learning methods, including video-action learning, imitation learning, and reinforcement learning.

• Design controlled experiments to improve task success, generalization, robustness, and data efficiency.

• Train and post-train models on robot demonstrations and multimodal sensor data; analyze failures and identify the data needed to improve performance.

• Build reproducible training and evaluation workflows with clear baselines, metrics, ablations, and experiment records.

• Test policies on physical robots and collaborate on inference, control integration, and deployment.

• Help set research priorities and communicate results and technical tradeoffs clearly.

Required Qualifications

• At least two first-author publications at top-tier conferences in machine learning, computer vision, or robotics. This is a required qualification.

• Research experience in robot learning, machine learning, computer vision, or a closely related field.

• Strong Python and PyTorch skills, including experience implementing, training, and debugging learning systems.

• Ability to design rigorous experiments, interpret results, and translate research ideas into working code.

• Experience with robotic systems or a strong interest in hands-on experimentation with physical hardware.

• A Ph.D. in a relevant field or equivalent research experience.

Preferred Experience

• World action models, video generation, diffusion policies, or vision-language-action models.

• Manipulation, teleoperation, reinforcement learning, or sim-to-real transfer.

• Large-scale training, multimodal datasets, and systematic policy evaluation.

Details and Compensation

• Location: South San Francisco, California; on-site.

• Employment: Full-time.

• Annual base salary: $200,000–$350,000 USD.

Join Us

Help build models that learn real jobs and robots that perform them reliably. Include links to at least two qualifying first-author conference publications and describe your contributions. Code, project write-ups, and demonstrations are welcome too.