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Responsibilities
As a key technology leader within the team, you will:
1. Define the overarching data and AI technology blueprint to support humanoid robot perception, decision-making, and control.
2. Design and implement scalable, high-availability data architectures for AI model training in robotics, materials, and component development.
3. Build and maintain robust data pipelines for the collection, storage, processing, and provisioning of large-scale, complex datasets.
4. Ensure data quality, governance, and compliance throughout the entire data lifecycle.
5. Provide expert guidance on AI model development and deployment across project teams.
6. Serve as a subject matter expert in data-driven methodologies and AI architecture.
7. Collaborate with motion control and application specialists to integrate sensory and control data for AI training and deployment.
8. Develop and maintain infrastructure for foundation models (e.g., LLMs, VLMs, VLAs) to enable robotic perception, reasoning, and action planning.
9. Build scalable pipelines for training and fine-tuning large models using both real-world and simulated data.
10. Integrate multimodal models (vision-language-action) into robotic systems to enable adaptive task execution.
11. Contribute to the development of simulation-to-real pipelines for humanoid robotics.
Qualifications
1. Master’s degree or higher in Computer Science, Data Engineering, Data Science, or a related field, 10+ years of experience in industry or academia.
2. Proven expertise in designing data architecture and developing data pipelines (batch and streaming).
3. Proficiency with cloud or on-premises data platforms (e.g., AWS, Azure, Hadoop, Spark).
4. Strong programming skills in Python and hands-on experience with machine learning frameworks.
5. Practical experience with foundation models (e.g., LLMs, VLMs, VLAs) or large-scale AI systems.
6. Solid understanding of data management best practices.
7. Excellent communication skills in English.
Nice-to-Haves:
- Experience with AI/ML techniques such as Reinforcement Learning and Deep Learning.
- Familiarity with robotics data (e.g., sensor data, simulation, telemetry).
- Experience in motion data processing and control signal modeling.
- Background in training, fine-tuning, or integrating foundation models for robotic applications.
- Knowledge of data governance and security standards.
- Understanding of digital twin workflows and control-policy optimization.