Description

In this role, you will design and build the infrastructure that powers advanced machine learning capabilities in robotics. Your work will involve optimizing training processes, developing APIs, and ensuring efficient resource allocation for real-world applications.

Requirements

• •Bachelor's degree in Computer Science, Robotics, Machine Learning, or related field
• •2 years of experience in software development with focus on MLOps or machine learning infrastructure
• •Proficiency in programming with Python and C++
• •Experience with containerization and orchestration technologies, specifically Docker and Kubernetes
• •Experience with deep learning frameworks such as TensorFlow, JAX, or PyTorch
• •Experience with cloud computing platforms like Google Cloud Platform
• •Basic Front-end experience
• •Master’s degree or PhD in Computer Science, Robotics, or related field
• •Comprehensive knowledge of the entire image processing workflow
• •Experience with MLOps toolkits such as Kubeflow
• •Hands on experience with CUDA optimization or deep learning optimization
• •Experience deploying machine learning models at scale in production environments
• •Familiarity with robotics systems or industrial automation hardware
• •Practical experience debugging distributed systems and optimizing network performance
• •Experience with accelerator orchestration (e.g., XManager)

Tasks

• •Design and implement scalable infrastructure for deep learning models
• •Optimize data loading and training speed for over 1000 GPU jobs
• •Build data pipelines for distributed computing of robotics data
• •Develop APIs and tools for integrating machine learning techniques
• •Lead efforts to open source models and engage with the community
• •Optimize compute resource allocation to reduce costs and latency
• •Create orchestration workflows for running jobs on GKE
• •Develop tools for model understanding and analysis

Tools & Technologies