Helix AI Engineer, Generative AI (AI Engineering)

TL;DR

Helix AI Engineer, Generative AI (AI Engineering): Building and scaling generative models that enable robots to understand, simulate, and interact with the physical world with an accent on training and deploying diffusion and generative models across vision, video, and multimodal domains. Focus on improving robot perception, world modeling, and prediction from raw sensory inputs.

Location: Requires 5 days/week in-office collaboration in San Jose, CA

Company

hirify.global is an AI robotics company developing autonomous general-purpose humanoid robots.

What you will do

• Design, train, and deploy large-scale generative models, with a focus on diffusion-based approaches for vision, video, and multimodal data.
• Develop models that improve robot perception, world modeling, and prediction from raw sensory inputs.
• Build generative systems for synthetic data creation, augmentation, and dataset scaling for robot learning.
• Optimize training pipelines for large-scale generative models across distributed systems.
• Integrate generative models into the full autonomy stack, working closely with data, training infrastructure, and agent teams.
• Evaluate model quality, robustness, and generalization across real-world scenarios.

Requirements

• Experience training and deploying generative models (diffusion, autoregressive, or related approaches) at scale.
• Strong understanding of modern deep learning techniques for vision and/or multimodal systems.
• Proficiency in Python and deep learning frameworks such as PyTorch.
• Experience working with large-scale datasets and distributed training systems.
• Strong experimental rigor and ability to iterate quickly on model performance.
• Solid software engineering skills and ability to build reliable, maintainable systems.

Nice to have

• Experience with diffusion models for image or video generation.
• Experience with multimodal foundation models (vision-language or vision-language-action).
• Background in synthetic data generation or simulation for robotics or embodied AI.
• Experience optimizing large-scale training (multi-node, GPU clusters, etc.).
• Familiarity with 3D, video prediction, or world models.