Figure is an AI Robotics company developing a general purpose humanoid. Our humanoid robot is designed for commercial tasks and the home. We are based in San Jose, CA and require 5 days/week in-office collaboration. It’s time to build.

Figure’s vision is to deploy autonomous humanoids at a global scale. Our Helix team is looking for an experienced Training Infrastructure Engineer, to take our infrastructure to the next level. This role is focused on managing the training cluster, implementing distributed training algorithms, data loaders, and developer tools for AI researchers. The ideal candidate has experience building tools and infrastructure for a large-scale deep learning system.

Responsibilities

• Design, deploy, and maintain Figure's training clusters
• Architect and maintain scalable deep learning frameworks for training on massive robot datasets
• Work together with AI researchers to implement training of new model architectures at a large scale
• Implement distributed training and parallelization strategies to reduce model development cycles
• Implement tooling for data processing, model experimentation, and continuous integration

Requirements

• Strong software engineering fundamentals
• Bachelor's or Master's degree in Computer Science, Robotics, Engineering, or a related field
• Experience with Python and PyTorch
• Experience managing HPC clusters for deep neural network training
• Minimum of 4 years of professional, full-time experience building reliable backend systems

Bonus Qualifications

• Experience managing cloud infrastructure (AWS, Azure, GCP)
• Experience with job scheduling / orchestration tools (SLURM, Kubernetes, LSF, etc.)
• Experience with configuration management tools (Ansible, Terraform, Puppet, Chef, etc.)
  
  

The US base salary range for this full-time position is between $150,000 - $350,000 annually.

The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.