METRIX IT SOLUTIONS INC
Applied Scientist (Advanced AI) - AI & Robotics
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an Applied Scientist (Advanced AI) - AI & Robotics in Mountain View, California. It's a long-term contract requiring a PhD or Master's with relevant experience, strong Python and PyTorch skills, and expertise in robotics and AI architectures.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
March 26, 2026
🕒 - Duration
Unknown
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🏝️ - Location
On-site
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Mountain View, CA
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🧠 - Skills detailed
#Strategy #Debugging #PyTorch #Transformers #Batch #ML (Machine Learning) #Automation #Python #Deployment #"ETL (Extract #Transform #Load)" #Process Automation #Data Strategy #AI (Artificial Intelligence)
Role description
Role: Applied Scientist (Advanced AI) - AI & Robotics
Location : Mountain View, California
Longterm Contract
Skills required:
Digital : Python
Digital : Artificial Intelligence(AI)
Digital : Robotic Process Automation - Automation Anywhere
Experience Range:
4 to 6 years
Pre-Screening Questionnaire:
Pre-screen questions you'd like for the Suppliers to ask potential candidates and include the answer on the coversheet. – Have you worked in robotics, robot learning, or embodied AI domain?
Must Have Technical/Functional Skills:
PhD in a relevant STEM field, or Master’s with equivalent industry experience in robotics, robot learning, or embodied AI.
Proven experience building and deploying machine learning models on robotic systems—including training, evaluation, and real-world execution or simulation.
Deep understanding of modern AI architectures (e.g., Transformers, diffusion models, VLM/VLAs, CNNs) with strong experience training models at scale.
Strong PyTorch implementation skills, including authoring custom modules, batching, debugging, and performance optimization.
Practical experience with ROS/ROS2 and integrating learned policies into manipulation or motion control workflows.
Demonstrated impact via robot learning publications, open-source contributions, or production robotics deployments.
Roles & Responsibilities:
Design and implement advanced robot learning architectures (e.g., diffusion policies, ACT, VLM/VLA-guided agents, imitation learning) to support dexterous manipulation, path planning, and autonomous task sequencing.
Develop end-to-end policy training pipelines, integrating multi-modal sensory data (RGB, depth, proprioception, force/torque, LiDAR, tactile inputs) with control outputs.
Build policy inference and closed-loop control that connect perception, planning, and execution on physical robotic platforms.
Apply and extend large-scale architectures—LLMs, VLM/VLAs, diffusion models—to embodied tasks, grounding, and sim-to-real adaptation.
Collaborate with cross-functional teams to deploy robot policies on hardware, ensuring robustness, repeatability, and safety.
Lead data strategy for demonstrations, teleoperation, simulation pipelines, and evaluation frameworks for manipulation policies.
Stay current with embodied AI research and share insights internally through discussion, mentorship, and technical presentations
Role: Applied Scientist (Advanced AI) - AI & Robotics
Location : Mountain View, California
Longterm Contract
Skills required:
Digital : Python
Digital : Artificial Intelligence(AI)
Digital : Robotic Process Automation - Automation Anywhere
Experience Range:
4 to 6 years
Pre-Screening Questionnaire:
Pre-screen questions you'd like for the Suppliers to ask potential candidates and include the answer on the coversheet. – Have you worked in robotics, robot learning, or embodied AI domain?
Must Have Technical/Functional Skills:
PhD in a relevant STEM field, or Master’s with equivalent industry experience in robotics, robot learning, or embodied AI.
Proven experience building and deploying machine learning models on robotic systems—including training, evaluation, and real-world execution or simulation.
Deep understanding of modern AI architectures (e.g., Transformers, diffusion models, VLM/VLAs, CNNs) with strong experience training models at scale.
Strong PyTorch implementation skills, including authoring custom modules, batching, debugging, and performance optimization.
Practical experience with ROS/ROS2 and integrating learned policies into manipulation or motion control workflows.
Demonstrated impact via robot learning publications, open-source contributions, or production robotics deployments.
Roles & Responsibilities:
Design and implement advanced robot learning architectures (e.g., diffusion policies, ACT, VLM/VLA-guided agents, imitation learning) to support dexterous manipulation, path planning, and autonomous task sequencing.
Develop end-to-end policy training pipelines, integrating multi-modal sensory data (RGB, depth, proprioception, force/torque, LiDAR, tactile inputs) with control outputs.
Build policy inference and closed-loop control that connect perception, planning, and execution on physical robotic platforms.
Apply and extend large-scale architectures—LLMs, VLM/VLAs, diffusion models—to embodied tasks, grounding, and sim-to-real adaptation.
Collaborate with cross-functional teams to deploy robot policies on hardware, ensuring robustness, repeatability, and safety.
Lead data strategy for demonstrations, teleoperation, simulation pipelines, and evaluation frameworks for manipulation policies.
Stay current with embodied AI research and share insights internally through discussion, mentorship, and technical presentations
