Helix AI Engineer, Modeling

Figure; San Jose (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

A robotics company in San Jose is seeking a Helix AI Engineer, Modeling to lead the design of core model architectures for humanoid autonomy. This role involves developing innovative modeling approaches across vision, language, and action, and requires a strong background in deep learning and modern model architectures. Candidates should be proficient in Python and have a deep understanding of multimodal systems. The job will be conducted in-office five days a week.

Qualifications

• Experience designing and training deep learning models for various systems.
• Strong understanding of transformer architectures and related approaches.
• Proficiency in Python and experience with deep learning frameworks.

Responsibilities

• Design and develop model architectures for perception, reasoning, and action.
• Advance multimodal learning approaches for structured representations.
• Collaborate with various teams to integrate modeling advances.

Skills

Deep learning models design and training

Modern model architectures

Python proficiency

Software engineering skills

Tools

PyTorch

Job description

Figure is an AI robotics company developing autonomous general-purpose humanoid robots. Our goal is to build embodied AI systems that can perceive, reason, and act in the real world. Figure is headquartered in San Jose, CA, and this role requires 5 days/week in-office collaboration.

Our Helix team is responsible for developing the core AI systems that power humanoid autonomy. We are looking for a Helix AI Engineer, Modeling to design and advance the core model architectures and learning approaches that enable perception, reasoning, and action in embodied systems.

This role focuses on developing new modeling approaches across vision, language, and action—spanning representation learning, multimodal fusion, and model capabilities that directly impact robot intelligence.

Responsibilities

• Design and develop model architectures for perception, reasoning, and action across multimodal inputs (e.g., vision, language, proprioception)
• Build models that learn structured representations of the world, including objects, dynamics, and interactions
• Advance multimodal learning approaches, including fusion, alignment, and cross-modal reasoning
• Improve model capabilities in areas such as generalization, robustness, and long‑horizon reasoning
• Work across the model lifecycle, from initial research and prototyping to training and deployment
• Collaborate closely with pretraining, video, generative, RL, and robot learning teams to integrate modeling advances into the full autonomy stack
• Design experiments and evaluation frameworks to understand model behavior and guide iteration
• Contribute to the development of new modeling paradigms for embodied AI systems

Requirements

• Experience designing and training deep learning models for vision, language, or multimodal systems
• Strong understanding of modern model architectures (e.g., transformers and related approaches)
• Experience improving model performance through architectural innovation and experimentation
• Proficiency in Python and deep learning frameworks such as PyTorch
• Strong experimental rigor and ability to iterate on model design and performance
• Solid software engineering skills and ability to build reliable, maintainable systems
• Ability to operate independently and drive ambiguous, high‑impact technical problems

Bonus Qualifications

• Experience with multimodal models (vision-language or vision-language-action systems)
• Background in representation learning, world models, or structured prediction
• Experience working on frontier models at companies such as OpenAI, Google DeepMind, Anthropic, Meta, or xAI
• Familiarity with embodied AI, robotics, or real-world ML systems
• Experience with large-scale training or distributed systems
• Publication record in machine learning, computer vision, NLP, or multimodal AI

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.