Our mission

At Humanoid we strive to create the world's leading, commercially scalable, safe, and advanced humanoid robots that seamlessly integrate into daily life and amplify human capacity.

Vision

In a world where artificial intelligence opens up new horizons, our faith in its potential unveils a new outlook where, together, humans and machines build a new future filled with knowledge, inspiration, and incredible discoveries. The development of a functional humanoid robot underpins an era of abundance and well-being where poverty will disappear, and people will be able to choose what they want to do. We believe that providing a universal basic income will eventually be a true evolution of our civilization.

Solution

As the demands on our built environment rise, labour shortages loom. With the world’s workforce increasingly moving away from undesirable tasks, the manufacturing, construction, and logistics industries critical to our daily lives are left exposed. By deploying our general-purpose humanoid robots in environments deemed hazardous or monotonous, we envision a future where human well-being is safeguarded while closing the gaps in critical global labour needs.

Responsibilities:

• Oversee the end-to-end lifecycle of AI/ML models, from development to deployment and monitoring.
• Ensure the reliability, scalability, and security of AI/ML infrastructure.
• Implement best practices for version control, testing, and continuous integration/continuous deployment (CI/CD) for AI/ML models.
• Troubleshoot and resolve issues related to AI/ML model performance and infrastructure.
• Stay up-to-date with the latest advancements in AI/ML operations, tools, and best practices.
• Provide technical support and guidance to data scientists and other team members on AI/ML Ops practices.
• Maintain comprehensive documentation for AI/ML infrastructure, processes, and best practices.

Expertise

• Proven experience as an AI/ML Ops Engineer or in a similar role, with a strong understanding of AI/ML lifecycle management.
• Proficiency in programming languages such as Python, and experience with AI/ML frameworks (e.g., TensorFlow, PyTorch).
• Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and containerization tools (e.g., Docker, Kubernetes).
• Strong knowledge of CI/CD pipelines, automation tools, and version control systems (e.g., Git).
• Familiarity with data engineering concepts, including data pipelines, ETL processes, and big data technologies.