Director of AI Infrastructure

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

Preferred ?ualifications