The following questions reflect the patterns and themes frequently encountered by candidates interviewing for AI roles at Figure AI. Use these to guide your study sessions, focusing on the underlying concepts rather than memorizing exact answers.

Resume and Experience Deep Dive

Interviewers use these questions to gauge the depth of your hands-on experience and your ability to articulate complex engineering decisions.

• Walk me through the most challenging AI project on your resume. What was your specific role?
• Why did you choose that specific model architecture for your project instead of a simpler baseline?
• Tell me about a time your model failed in a real-world or testing environment. How did you diagnose the root cause?
• How do you balance the trade-off between model accuracy and inference speed in your past work?
• Describe a situation where you had to work with messy or incomplete data. How did you handle it?

Reinforcement and Imitation Learning

These questions test your mastery of the algorithms that are critical for robotic control and autonomous behavior.

• Explain the core differences between Behavioral Cloning and Reinforcement Learning.
• How does the DAgger algorithm work, and what specific problem does it solve in Imitation Learning?
• Can you derive the policy gradient theorem?
• What is the exploration-exploitation tradeoff, and how is it managed in algorithms like PPO or SAC?
• How would you approach the sim-to-real transfer problem for a robotic manipulation task?

Coding and ML Systems

These assess your ability to implement algorithms cleanly and design scalable infrastructure for training and deployment.

• Write a Python script to implement a basic self-attention mechanism from scratch.
• How would you debug a PyTorch training loop that is running out of memory (OOM) before the first epoch finishes?
• Design a system to collect, store, and sample multi-modal data (video and robot joint states) for training an Imitation Learning model.
• Explain how you would optimize a trained model for deployment on an edge device with limited compute.

Reinforcement Learning and Imitation Learning

Given the nature of humanoid robotics, modern control and learning algorithms are paramount. You will face direct, targeted questions on the algorithms that allow robots to learn complex behaviors.

Be ready to go over:

• Reinforcement Learning (RL) fundamentals – Core concepts like Markov Decision Processes (MDPs), policy gradients, and value functions.
• Imitation Learning – Techniques like Behavioral Cloning and DAgger, and how to handle compounding errors when a robot deviates from its training distribution.
• Algorithm specifics – Deep understanding of algorithms commonly used in continuous control, such as PPO (Proximal Policy Optimization) or SAC (Soft Actor-Critic).

Example questions or scenarios:

• "Explain the difference between on-policy and off-policy reinforcement learning. When would you use one over the other in a robotics context?"
• "How do you mitigate the distribution shift problem in standard behavioral cloning?"
• "Walk me through the mathematical formulation of PPO and explain why the clipping mechanism is necessary."

Machine Learning Engineering and Systems

Training a model is only half the battle; deploying it to a walking, grasping robot requires exceptional engineering rigor. Interviewers will evaluate your ability to write clean, performant code and structure scalable ML systems.

Be ready to go over:

• Framework proficiency – Deep knowledge of PyTorch, including distributed training and memory management.
• Optimization – Techniques for reducing inference latency, such as quantization, pruning, or TensorRT deployment.
• Data pipelines – Managing large-scale, multi-modal datasets (e.g., video, proprioception data) efficiently.

Example questions or scenarios:

• "How would you optimize a PyTorch model to ensure it meets a strict 10ms inference latency budget on edge hardware?"
• "Design a data ingestion pipeline that synchronizes high-framerate camera feeds with low-latency joint encoder data."
• "Explain how you would set up a distributed training job across multiple GPU nodes. What are the common bottlenecks?"

6. Key Responsibilities

As an AI Engineer at Figure AI, your day-to-day work revolves around pushing the boundaries of what humanoid robots can autonomously achieve. You will be responsible for designing, training, and evaluating state-of-the-art neural networks that govern robot behavior, ranging from low-level joint control to high-level semantic reasoning.

Collaboration is a massive part of this role. You will frequently partner with the robotics engineering team to integrate your models onto physical hardware, participating in real-world testing and debugging sessions. This requires interpreting sensor data, analyzing robot telemetry, and iterating on your models based on physical performance rather than just validation metrics.

Furthermore, you will drive the development of scalable training infrastructure. This includes curating massive datasets from both simulation and real-world teleoperation, setting up robust evaluation pipelines, and ensuring that the transition from simulation to reality (sim-to-real) is as seamless as possible.

7. Role Requirements & Qualifications

To be a competitive candidate for the AI Engineer position, you need a strong mix of academic depth and practical software engineering capability.

• Must-have technical skills – Expert-level proficiency in Python and PyTorch. A deep theoretical and practical understanding of Reinforcement Learning, Imitation Learning, and Deep Learning architectures (such as Transformers or CNNs).
• Must-have experience – Proven experience training and deploying complex machine learning models. A track record of owning end-to-end ML pipelines, from data collection to inference optimization.
• Nice-to-have skills – Experience with C++, ROS (Robot Operating System), or CUDA. Background in robotics, specifically addressing sim-to-real transfer, kinematics, or computer vision for manipulation tasks.
• Soft skills – Exceptional communication skills to explain complex AI behaviors to cross-functional teams. A high degree of adaptability and a bias toward action in a fast-paced, startup environment.

8. Frequently Asked Questions

Q: How long does the interview process typically take? The process at Figure AI is highly efficient. Candidates often report moving from the initial application or recruiter screen to a final decision within 2 to 3 weeks. You should be prepared to schedule interviews quickly.

Q: Are the technical rounds mostly LeetCode-style or domain-specific? While you may encounter some general coding questions, the technical rounds are heavily skewed toward domain-specific knowledge. Expect deep dives into your resume, applied machine learning systems, and specific algorithms like Reinforcement Learning and Imitation Learning.

Q: Is this role remote or hybrid? Because Figure AI is building physical humanoid robots, engineering roles generally require a strong onsite presence at their California headquarters. Working directly with the hardware and cross-functional teams in the lab is critical for an AI Engineer.

Q: What differentiates a successful candidate from an average one? Successful candidates demonstrate a rare combination of deep theoretical ML knowledge and "scrappy" engineering pragmatism. They don't just know how to train a model; they know how to make it run fast, debug it when the physical robot falls over, and iterate rapidly.

9. Other General Tips

• Own Your Narrative: Because the first technical round heavily focuses on your resume, practice walking through your projects out loud. You must be able to concisely explain the problem, your architectural choices, and the measurable impact of your work within minutes.
• Brush Up on the Math: Figure AI interviewers will ask you to explain the mechanics of complex algorithms. Do not rely solely on high-level intuition; ensure you can comfortably discuss the mathematical formulations of core RL and Imitation Learning algorithms.