Helix AI Engineer, Pretraining (AI)

TL;DR

Helix AI Engineer, Pretraining (AI): Building large-scale foundation models that learn from diverse data sources including text, images, video, and robot-collected experience with an accent on generalization, reasoning, and adaptability. Focus on scaling laws, dataset mixture design, and training dynamics for frontier models.

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 and train large-scale foundation models across multimodal data (e.g., text, vision, and robot data).
• Develop pretraining strategies that improve generalization, reasoning, and transfer to downstream embodied tasks.
• Build and optimize large-scale distributed training pipelines across multi-node GPU clusters.
• Collaborate closely with video, generative, agent, and robot learning teams to integrate pretrained models into the autonomy stack.
• Design evaluation frameworks to measure reasoning ability, robustness, and cross-domain generalization.
• Contribute to post-training approaches including fine-tuning, alignment, and model adaptation.

Requirements

• Experience training large-scale foundation models or working on pretraining for LLMs or multimodal systems.
• Strong understanding of modern deep learning architectures, especially transformers.
• Experience with large-scale distributed training and optimization.
• Proficiency in Python and deep learning frameworks such as PyTorch.
• Solid software engineering skills and ability to build scalable, reliable systems.
• Ability to operate independently and drive ambiguous, high-impact technical problems.

Nice to have

• Experience working on frontier foundation models at companies such as Anthropic, OpenAI, Google DeepMind, or xAI.
• Experience with multimodal pretraining (vision-language or vision-language-action models).
• Background in scaling laws, dataset curation, and large-scale data mixture optimization.
• Experience with post-training techniques such as RLHF, reward modeling, or alignment methods.
• Familiarity with embodied AI, robotics, or real-world deployment constraints.