Helix AI Engineer, Agentic Systems

Figureai; San Jose (CA); On-site; USD 150,000 - 350,000; Full time

Job summary

Figure AI, headquartered in San Jose, CA, is seeking a Helix AI Engineer to develop multimodal reasoning systems for autonomous humanoid robots. The role entails designing and deploying agents that leverage sensory inputs to perform complex tasks reliably.

The ideal candidate will have experience building embedding AI systems, strong Python skills, and the ability to work independently. This full-time position offers a competitive salary range between $150,000 - $350,000 annually.

Qualifications

• Experience building autonomous agents that run continuously and complete multi-step tasks.
• Strong proficiency in Python and experience with modern deep learning frameworks.
• Ability to work independently and own complex technical problems end-to-end.

Responsibilities

• Design, train, and deploy multimodal agents that operate autonomously.
• Build agents that reason from raw sensory inputs to structured actions.
• Implement episodic memory systems for persistent state and long-horizon reasoning.

Skills

Building autonomous agents

Multimodal reasoning

Python programming

Deep learning frameworks

Software engineering

Job description

Figure AI is an AI robotics company developing autonomous general-purpose humanoid robots. The goal of the company is to ship humanoid robots with human-level intelligence. Its robots are engineered to perform a variety of tasks in the home and commercial markets. Figure is headquartered in San Jose, CA.

Our goal is to create embodied AI systems that can perceive the world through pixels, reason over memory, and reliably execute complex tasks over minutes to hours in real environments. We are looking for a Helix AI Engineer, Agentic Systems experienced in building multimodal reasoning systems—agents that operate autonomously from raw sensory input, maintain episodic memory, plan over long horizons, and execute reliably within structured evaluation harnesses, e.g. pixels-to-actions computer use agents. This role focuses on developing the agent architectures and infrastructure that enable robots to function as persistent, reliable embodied agents in the real world.

Responsibilities

• Design, train, and deploy multimodal agents that operate autonomously for hours to days
• Build agents that reason from raw sensory inputs (pixels, environment state, proprioception) to structured actions
• Implement episodic memory systems for persistent state, retrieval, and long-horizon reasoning
• Develop planning, reasoning, and tool-use mechanisms for multi-step task execution
• Build reliable perception → reasoning → action loops with strong stability and failure recovery
• Design evaluation harnesses, benchmarks, and metrics to measure agent reasoning, planning, and reliability
• Design and run data studies across the training lifecycle, including pretraining, mid-training, and post-training
• Apply reinforcement learning, reward modeling, and post-training techniques to improve agent reasoning and reliability in real-world environments
• Develop evaluation frameworks and benchmarks to measure robot reasoning, planning, and task success across diverse scenarios
• Build infrastructure for scalable model training, distributed experimentation, and agent evaluation
• Work closely with other teams to integrate agent models into the full humanoid autonomy stack

Requirements

• Experience building autonomous agents that run continuously and complete multi-step tasks
• Experience developing agents that reason from pixel inputs or raw environment observations
• Experience implementing agent memory, planning, reasoning, or tool-use systemsExperience training or fine-tuning multimodal or foundation models
• Strong proficiency in Python and modern deep learning frameworks (e.g., PyTorch)
• Strong experimental rigor and ability to design, analyze, and iterate on ML systems
• Strong software engineering skills and ability to build reliable, maintainable systems
• Ability to work independently and own complex technical problems end-to-end

Bonus Qualifications

• Experience with embodied AI, robotics learning, or robot policy training
• Experience building multimodal foundation models (vision-language or vision-language-action)
• Background in agentic AI systems or long-horizon planning architectures
• Experience working with large-scale distributed training systems
• Publication record in machine learning, robotics, or embodied AI
• Passion for building autonomous humanoid robots that operate in the real world

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.