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 systems
• Experience 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.

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. 

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, Reinforcement Learning to develop learning systems that enable robots to acquire skills through interaction, feedback, and experience.

This role focuses on applying and advancing reinforcement learning across simulation and real-world environments—improving policy performance, robustness, and long-horizon decision-making in embodied systems.

Responsibilities

• Design and implement reinforcement learning algorithms for embodied agents operating in real-world and simulated environments
• Train policies that learn from interaction, feedback, and large-scale experience across diverse tasks
• Develop reward modeling, credit assignment, and exploration strategies for complex, long-horizon behaviors
• Improve policy robustness to real-world challenges such as noise, partial observability, and environment variability
• Work across online and offline RL settings, including learning from large-scale logged robot data
• Collaborate closely with pretraining, video, generative, agent, and robot learning teams to integrate RL into the full autonomy stack
• Build scalable training systems for RL, including distributed rollouts, simulation infrastructure, and experiment management
• Design evaluation frameworks to measure policy performance, stability, and generalization

Requirements

• Experience developing and applying reinforcement learning algorithms in complex environments
• Strong understanding of RL fundamentals (e.g., policy optimization, value methods, model-based RL)
• Experience training policies in simulation and/or real-world systems
• Proficiency in Python and deep learning frameworks such as PyTorch
• Experience with large-scale experimentation and distributed training systems
• Strong experimental rigor and ability to diagnose and improve learning systems
• Solid software engineering skills and ability to build scalable, reliable systems
• Ability to operate independently and drive ambiguous, high-impact technical problems

Bonus Qualifications

• Experience applying RL to robotics, control systems, or embodied AI
• Experience with large-scale RL infrastructure (distributed rollouts, simulation at scale)
• Background in offline RL, imitation learning, or hybrid learning approaches
• Experience with reward modeling or human-in-the-loop learning
• Experience at leading AI labs such as OpenAI, Google DeepMind, Anthropic, or xAI
• Familiarity with robotics systems, simulation environments, or real-world deployment constraints
• Publication record in reinforcement learning, machine learning, or robotics

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. 

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, Pretraining to build large-scale foundation models that learn from diverse data sources including text, images, video, and robot-collected experience.

This role focuses on advancing pretraining methods that enable generalization, reasoning, and adaptability—forming the backbone for downstream capabilities in perception, planning, and action.

Responsibilities

• 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
• Explore and implement architectures including transformer-based and emerging foundation model paradigms
• Work on scaling laws, dataset mixture design, and training dynamics for frontier models
• 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
• Strong experimental rigor and ability to iterate on model design and training strategies
• Solid software engineering skills and ability to build scalable, reliable systems
• Ability to operate independently and drive ambiguous, high-impact technical problems

Bonus Qualifications

• 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
• Publication record in machine learning, 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. 

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, Video Pretraining to lead the development of large-scale video foundation models trained on diverse real-world and robot-collected data.

This role focuses on pretraining models that learn from raw video—capturing motion, interaction, and temporal structure—to enable downstream capabilities in perception, prediction, and embodied reasoning.

Responsibilities

• Design and train large-scale video foundation models on diverse datasets spanning internet-scale video and robot-collected data
• Develop pretraining strategies that capture temporal dynamics, motion, and object interaction from raw video sequences
• Build models that learn transferable representations for downstream tasks such as perception, tracking, prediction, and control
• Explore architectures for video understanding and generation, including transformer-based and diffusion-based approaches
• Implement efficient data pipelines and training strategies for high-throughput video ingestion and large-scale distributed training
• Optimize model performance across compute, memory, and training efficiency constraints
• Collaborate closely with generative modeling, agent, and robot learning teams to integrate pretrained models into the autonomy stack
• Design evaluation frameworks and benchmarks to measure temporal understanding, prediction quality, and generalization

Requirements

• Experience training large-scale models on video data or other high-dimensional sequential modalities
• Strong understanding of modern deep learning architectures for video, vision, or multimodal systems
• Experience with large-scale pretraining, including dataset curation, training dynamics, and scaling laws
• Proficiency in Python and deep learning frameworks such as PyTorch
• Experience working with distributed training systems and large GPU clusters
• Strong experimental rigor and ability to iterate quickly on model design and training strategies
• Solid software engineering skills and ability to build scalable, reliable systems
• Ability to operate independently and drive ambiguous, high-impact research directions

Bonus Qualifications

• Experience working on frontier video models or multimodal foundation models
• Background in video diffusion, autoregressive video modeling, or world models
• Experience at leading AI labs such as OpenAI, Google DeepMind, Google, ByteDance, Midjourney, or Adobe
• Experience with large-scale dataset construction and filtering for video pretraining
• Familiarity with robotics, embodied AI, or learning from egocentric / first-person video
• Publication record in machine learning, computer vision, 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. 

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, Generative AI to build and scale generative models that enable robots to understand, simulate, and interact with the physical world. This role focuses on training and deploying diffusion and generative models across vision, video, and multimodal domains, with applications spanning perception, data generation, and model-based reasoning.

Responsibilities

• 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
• Explore and implement state-of-the-art techniques in diffusion, generative modeling, and multimodal foundation models
• Optimize training pipelines for large-scale generative models across distributed systems
• Work closely with data, training infrastructure, and agent teams to integrate generative models into the full autonomy stack
• Evaluate model quality, robustness, and generalization across real-world scenarios
• Contribute to the design of scalable experimentation frameworks for generative model development

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
• Ability to operate independently and own ambiguous, high-impact technical problems

Bonus Qualifications

• 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
• Prior work in robotics, embodied AI, or real-world ML systems
• Publication record in machine learning, computer vision, or generative modeling

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. 

Figure is an AI Robotics company developing a general purpose humanoid. Our humanoid robot is designed for commercial tasks and the home. We are based in San Jose, CA and require 5 days/week in-office collaboration. It’s time to build.

Figure’s vision is to deploy autonomous humanoids at a global scale. Our Helix team is seeking an experienced AI Tooling Engineer to enhance our internal, web-based data and AI training tools. This role focuses on developing intuitive web interfaces that support key AI research functions, including robot data annotation, training dataset visualization, and experiment tracking. The ideal candidate has experience building rich, interactive web interfaces using React and TypeScript.

Responsibilities

• Design and build intuitive web interfaces for robot data annotation, datasets visualization, and experiment tracking
• Utilize data-driven techniques to optimize interfaces for efficiency and fast iteration cycles
• Integrate AI models to automate manual tasks
• Work together with AI researchers, robot operators, and annotators to support new user experiences

Requirements

• Strong software engineering fundamentals
• Bachelor's or Master's degree in Computer Science, Robotics, Engineering, or a related field
• Minimum of 4 years of professional, full-time experience building rich, interactive web interfaces
• Proficiency in React and TypeScript

Bonus Qualifications

• Experience using data stores (Postgres, MySQL, ElasticSearch, Redis, etc.)
• Experience managing cloud infrastructure (AWS, Azure, GCP)
• Experience with Tailwind CSS
• Experience building data annotation and dataset management tools.
  
  

The US base salary range for this full-time position is between $150,000 - $250,000 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.

Figure 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.

Figure’s vision is to deploy autonomous humanoids at a global scale. Our Helix team is looking for an experienced Training Infrastructure Engineer, to take our infrastructure to the next level. This role is focused on managing the training cluster, implementing distributed training algorithms, data loaders, and developer tools for AI researchers. The ideal candidate has experience building tools and infrastructure for a large-scale deep learning system.

Responsibilities

• Design, deploy, and maintain Figure's training clusters
• Architect and maintain scalable deep learning frameworks for training on massive robot datasets
• Work together with AI researchers to implement training of new model architectures at a large scale
• Implement distributed training and parallelization strategies to reduce model development cycles
• Implement tooling for data processing, model experimentation, and continuous integration

Requirements

• Strong software engineering fundamentals
• Bachelor's or Master's degree in Computer Science, Robotics, Engineering, or a related field
• Experience with Python and PyTorch
• Experience managing HPC clusters for deep neural network training
• Minimum of 4 years of professional, full-time experience building reliable backend systems

Bonus Qualifications

• Experience managing cloud infrastructure (AWS, Azure, GCP)
• Experience with job scheduling / orchestration tools (SLURM, Kubernetes, LSF, etc.)
• Experience with configuration management tools (Ansible, Terraform, Puppet, Chef, etc.)
  
  

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.

Figure is an AI Robotics company developing a general purpose humanoid. Our humanoid robot is designed for commercial tasks and the home. We are based in San Jose and require 5 days/week in-office collaboration. It’s time to build.

Figure’s vision is to deploy autonomous humanoids at a global scale. Our Helix team is looking for an experienced Data Infrastructure Engineer, to take our AI data infrastructure to the next level. This role is focused on building tools and software components that offload, store, manipulate and provide access to robot data, managing on premise and cloud resources, and providing production support for this infrastructure. The ideal candidate has experience building tools and infrastructure for a large-scale autonomous / deep learning system.

Responsibilities

• Design, build, and maintain tools and software components that offload, store, manipulate and provide access to robot data
• Architect and manage storage and compute resources across on-premise and cloud environments at a massive scale.
• Implement optimal data transmission and storage solutions for all stages of Figure’s data pipeline from recording to neural network training
• Work together with AI researchers to support new kinds of data workflows

Requirements

• Strong software engineering fundamentals
• Bachelor's or Master's degree in Computer Science, Robotics, Engineering, or a related field
• Minimum of 4 years of professional, full-time experience building reliable backend systems
• Experience with Linux and command line tools
• Experience with Python
• Experience using and managing data stores (Postgres, MySQL, ElasticSearch, Redis, etc.).

Bonus Qualifications

• Experience managing cloud infrastructure (AWS, Azure, GCP)
• Experience with job scheduling / orchestration tools (SLURM, Kubernetes, LSF, etc.)
• Experience with configuration management tools (Ansible, Terraform, Puppet, Chef, etc.)
• Experience building data annotation and dataset management tools.
  
  

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.

Helix AI Engineer, Robot Learning

Figure 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.

We are looking for a Helix AI Engineer, Robot Learning with a strong robotics learning background to help develop and improve our visuomotor manipulation policies, with a heavy emphasis on real-robot deployment.

Responsibilities

• Design, train, evaluate, and deploy learning-based visuomotor policies for humanoid robot manipulation
• Develop manipulation behaviors such as grasping, pick-and-place, object reorientation, door opening, bimanual manipulation, and basic assembly
• Apply and extend techniques including behavior cloning, reinforcement learning, and VLA reasoning
  
• Train models that are robust to real-world challenges such as sensor noise, partial observability, contact dynamics, and environment variability
• Own the full pipeline from data collection on real robots to model training, evaluation, and deployment
• Work closely with simulation and digital twin tooling where useful, while prioritizing real-world performance and transfer
  
• Collaborate with perception, controls, systems, and hardware teams to integrate policies into a full autonomy stack
• Evaluate tradeoffs between learning-based and classical approaches and make principled design decisions
• Write high-quality, well-tested software that ships to and runs reliably on physical humanoid robots
• Partner with integration and testing teams to continuously improve robustness, performance, and deployment velocity

Requirements

• Hands-on experience developing and deploying robot learning systems on real robots
  
• Strong background in robot manipulation and visuomotor control
  
• Experience with behavior cloning, reinforcement learning, or related learning-based manipulation methods
• Proficiency in Python and/or C++ for robotics and ML systems
• Experience with modern deep learning frameworks (e.g., PyTorch)
• Ability to design experiments, analyze failures, and iterate quickly in real-world robotic systems
• Solid understanding of the tradeoffs between classical robotics approaches and learning-based methods
• Thrive in fast-paced, ambiguous environments where solutions require exploration and ownership

Bonus Qualifications

• Experience deploying learning-based manipulation systems in commercial or production robotic systems
  
• Prior work on humanoids or highly dexterous robotic platforms
• Publication record in robot learning, manipulation, or embodied AI
• Experience leading projects or mentoring other engineers
• Passion for building autonomous humanoid robots that operate in the real world

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.