Train AI for the real world
Your robotics and spatial computing expertise shapes how AI interacts with physical environments. Join the talent pool for upcoming projects!
Physical AI, or embodied AI, are systems that perceive, navigate, and interact with the real world. These can't be built on text and image training alone.
Through Mindrift, contributors with robotics and computer vision expertise annotate egocentric video, label 3D point clouds, and evaluate robotic action sequences to help build AI that operates safely in physical environments.
Gartner named physical AI a top strategic technology trend for 2026, and demand for skilled contributors is accelerating.
Physical AI projects on Mindrift offer competitive rates matching the specialized expertise required. Projects are fully remote and flexible – contributors set their own schedule and participate at their own pace.
Competitive per-task rates
Hands-on AI experience
Flexible remote project
Global community
Tasks vary between projects, but generally center around annotating real-world spatial data, evaluating AI-generated action plans, and providing the feedback that teaches AI to operate in physical environments.
Who can join physical AI projects?
Physical AI projects require specialized expertise in spatial computing, robotics, or computer vision. These are advanced tasks for professionals who understand how machines perceive and interact with the three-dimensional world.
Robotics engineers
Professionals with experience in robotic manipulation, motion planning, or autonomous systems. You understand kinematics, grasp planning, and the gap between simulated and real-world performance.
Computer vision and 3D annotation specialists
Experts experienced with point cloud annotation, depth estimation, semantic segmentation, or volumetric labeling. You have worked with LiDAR, stereo vision, or structured-light sensor data.
Spatial computing and AR/VR professionals
Developers and researchers working in augmented reality, virtual reality, or spatial computing. You have hands-on experience with 3D scene understanding, SLAM, and real-time environment mapping.
Join the talent pool
Physical AI projects are in active development. Join the talent pool now and be among the first contributors notified when projects launch.
There is no commitment until you accept a project. Joining the pool takes minutes and ensures your profile is ready when opportunities matching your skills become available.
How to get started
1. Apply
Submit your CV and indicate your robotics, CV, or spatial computing expertise
2. Qualify
Complete a domain-specific assessment when projects launch
3. Onboard
Get platform access and project guidelines walkthrough.
4. Earn
Start completing tasks at your own pace
Why physical AI matters now
Physical AI represents the next frontier of AI training. Models that interact with the real world safely and reliably are still in early stages. The training data challenge for physical AI is fundamentally different: it requires 3D spatial understanding, temporal reasoning about actions and consequences, and the kind of real-world physics intuition that only domain experts bring.
Mindrift is building the contributor network for this emerging field. Over 20,000 experts worldwide have already joined the Mindrift community across coding, STEM, writing, and other domains, backed by Toloka AI. Physical AI is the next chapter. Learn more about AI trends shaping 2026 or explore all current openings.
Frequently asked questions
What is physical AI?
Physical AI is artificial intelligence systems that perceive, navigate, and interact with the physical world, including robots, autonomous vehicles, drones, AR/VR systems, and any AI that operates beyond a screen. Training these systems requires spatial data: 3D point clouds, egocentric video, depth maps, and real-world action sequences. The goal is to teach models how objects, environments, and physics work in three dimensions.
What kind of data will I work with?
Depending on the project, you may work with egocentric video from wearable cameras, LiDAR point clouds, depth sensor data, stereo vision captures, robotic action recordings, or simulated environment data. Tasks involve annotation, labeling, evaluation, and classification of spatial and temporal information.
Do I need robotics hardware?
No. All annotation and evaluation tasks are performed through the Mindrift platform using a computer and internet connection. You do not need to own or operate physical robots – your expertise in understanding robotic systems and spatial data is what matters.
When will projects be available?
Physical AI projects are in active development. Joining the talent pool now ensures you are notified as soon as projects matching your skills launch. Mindrift regularly opens new project types as client demand grows.
What background do I need?
Experience in robotics engineering, computer vision, 3D annotation, spatial computing, autonomous systems, or related fields. Familiarity with point cloud data, LiDAR, depth sensors, or egocentric vision is strongly preferred. A technical degree is helpful but practical experience with spatial data is the primary qualification.
Is this freelance?
Yes. Physical AI projects on Mindrift are fully remote and project-based. You set your own schedule, choose which tasks to complete, and participate alongside other commitments. There is no employment contract and this is not traditional employment — Mindrift contributors are independent freelancers.
Is Mindrift legitimate?
Yes. Mindrift is owned and operated by Toloka AI, a global leader in AI data since 2014, headquartered in Amsterdam. Over 20,000 experts from around the world have contributed to Mindrift projects for major technology companies. Payments are processed reliably twice a month.
Mindrift connects robotics and computer vision experts with physical AI training projects. Backed by Toloka AI, a global leader in AI data since 2014, with over 20,000 experts worldwide.
