Figure is a Bay Area robotics startup building AI-powered humanoid robots for industrial tasks such as warehouse and factory work.
<p><strong>Figure AI</strong> 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.</p> <p>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 <strong>Helix AI Engineer, Agentic Systems</strong> 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.</p> <h3><strong>Responsibilities</strong></h3> <ul> <li>Design, train, and deploy multimodal agents that operate autonomously for hours to days</li> <li>Build agents that reason from raw sensory inputs (pixels, environment state, proprioception) to structured actions</li> <li>Implement episodic memory systems for persistent state, retrieval, and long-horizon reasoning</li> <li>Develop planning, reasoning, and tool-use mechanisms for multi-step task execution</li> <li>Build reliable perception → reasoning → action loops with strong stability and failure recovery</li> <li>Design evaluation harnesses, benchmarks, and metrics to measure agent reasoning, planning, and reliability</li> <li>Design and run <strong>data studies across the training lifecycle</strong>, including pretraining, mid-training, and post-training</li> <li>Apply <strong>reinforcement learning, reward modeling, and post-training techniques</strong> to improve agent reasoning and reliability in real-world environments</li> <li>Develop evaluation frameworks and benchmarks to measure <strong>robot reasoning, planning, and task success across diverse scenarios</strong></li> <li>Build infrastructure for <strong>scalable model training, distributed experimentation, and agent evaluation</strong></li> <li>Work closely with other teams to integrate agent models into the full humanoid autonomy stack</li> </ul> <h2><span style="font-size: 12pt;"><strong>Requirements</strong></span></h2> <ul> <li>Experience building autonomous agents that run continuously and complete multi-step tasks</li> <li>Experience developing agents that reason from pixel inputs or raw environment observations</li> <li>Experience implementing agent memory, planning, reasoning, or tool-use systems</li> <li>Experience training or fine-tuning multimodal or foundation models</li> <li>Strong proficiency in Python and modern deep learning frameworks (e.g., PyTorch)</li> <li>Strong experimental rigor and ability to design, analyze, and iterate on ML systems</li> <li>Strong software engineering skills and ability to build reliable, maintainable systems</li> <li>Ability to work independently and own complex technical problems end-to-end</li> </ul> <h2><span style="font-size: 12pt;"><strong>Bonus Qualifications</strong></span></h2> <ul> <li>Experience with <strong>embodied AI, robotics learning, or robot policy training</strong></li> <li>Experience building <strong>multimodal foundation models (vision-language or vision-language-action)</strong></li> <li>Background in <strong>agentic AI systems or long-horizon planning architectures</strong></li> <li>Experience working with <strong>large-scale distributed training systems</strong></li> <li>Publication record in machine learning, robotics, or embodied AI</li> <li>Passion for building autonomous humanoid robots that operate in the real world</li> </ul> <p>The US base salary range for this full-time position is between $200,000 - $400,000 annually.</p> <p>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.</p>