Overview
On Site
$Open
Accepts corp to corp applications
Contract - W2
Contract - Long Term
Skills
AI
Robotics
Job Details
Cerebra Consulting Inc is a System Integrator and IT Services Solution provider with a focus on Big Data, Business Analytics, Cloud Solutions, Amazon Web Services, Salesforce, Oracle EBS, Peoplesoft, Hyperion, Oracle Configurator, Oracle CPQ, Oracle PLM and Custom Application Development. Utilizing solid business experience, industry-specific expertise, and proven methodologies, we consistently deliver measurable results for our customers. Cerebra has partnered with leading enterprise software companies and cloud providers such as Oracle, Salesforce, Amazon and able to leverage these partner relationships to deliver high-quality, end-to-end customer solutions that are targeted to the needs of each customer.
Staff Applied Scientist - AI C Robotics
Mountain View, CA/Onsite
Long Term
# of staff needed: up to 4
Our AI Research team is building end-to-end robot policies that enable dexterous manipulation in real-world environments. We are advancing embodied AI by integrating multimodal perception, robot learning architectures, and physical execution systems (Staff Applied Scientist - AI C Robotics). We aim to solve manipulation, autonomy, and simulation challenges at an industrial scale. As a Staff Applied Scientist, you will lead the development of core components of these embodied systems-from model design and training pipelines to integration with perception, motion control, and hardware. You will design, prototype, and deploy robot learning models that span perception, policy learning, simulation, and real-world execution, collaborating closely with robotics engineers, AI infrastructure teams, and production experts.
What You'll Do
- 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.
Required ?ualifications
- 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.
Preferred ?ualifications
- Experience developing robot learning systems for dexterous manipulation, multi-step task execution, or autonomous behaviors.
- Expertise in robotics perception, including 3D understanding, force sensing, tactile feedback, multimodal fusion, or affordance modeling.
- Familiarity with Isaac Sim, Mujoco, Gazebo, PyBullet, or custom simulators, and demonstrated ability to transfer policies to hardware.
- Experience adapting foundation models (VLM/VLAs, diffusion, instruction-following agents) for embodied control tasks.
- Track record of production-ready robotics systems, reproducible research artifacts, or deployments in physical environments.
Interested consultants can send their updated resumes to
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.