An AI Engineer at Foundation Robotics Labs sits at the absolute frontier of physical intelligence. Rather than working on purely digital software applications, you will develop the cognitive and physical control systems that allow humanoid robots and autonomous systems to perceive, reason, and act in the real world. This role bridges the gap between cutting-edge artificial intelligence and physical embodiment, translating complex mathematical models into real-time, reliable physical behaviors.

At Foundation Robotics Labs, the AI engineering organization is divided into specialized, highly collaborative tracks. Whether your focus is on SLAM (Simultaneous Localization and Mapping), RL & WBC (Reinforcement Learning & Whole-Body Control), VLM & VLA (Vision-Language Models & Vision-Language-Action), or World Models, your work directly impacts the physical capability and safety of our robotic platforms. The algorithms you design will run on physical hardware, navigating complex, unstructured human environments and executing high-dexterity manipulation tasks.

This is a highly interdisciplinary role requiring a unique blend of software engineering rigor, mathematical depth, and physical intuition. You will work closely with hardware designers, simulation engineers, and product teams to build a seamless vertical stack. The engineering culture here values first-principles thinking, rapid iteration on physical hardware, and code that is as performant as it is mathematically elegant.

The questions you will encounter during the Foundation Robotics Labs interview process are designed to test your technical depth, spatial reasoning, and software engineering foundations. While we tailor our technical rounds to your specific domain expertise, every candidate is expected to demonstrate strong problem-solving capabilities and a solid grasp of robotics fundamentals.

The following questions are representative of the patterns and technical challenges you will face, categorized by key focus areas.

Spatial AI, SLAM, and State Estimation

This category evaluates your understanding of how a robot perceives and tracks its position in a dynamic, three-dimensional world.

• How do you handle degeneracy in scan matching when a robot is traversing a long, featureless corridor?

To succeed in the Foundation Robotics Labs interview loop, you must prepare to demonstrate both deep specialized knowledge and broad systems-level engineering skills. We look for candidates who can think from first principles rather than relying on memorized patterns.

First-Principles Engineering – You must understand the underlying physics and mathematics of your code. Be ready to derive basic equations, explain the trade-offs of different algorithmic approaches, and justify your architectural decisions using physical constraints.

Production-Grade Execution – We write software that controls heavy, fast-moving physical hardware. Your code must be clean, robust, and highly optimized. You will be evaluated on your ability to write clean C++ or Python, handle edge cases, and design APIs that are easy to test and maintain.

Systemic Architecture Design – Robots are complex systems of interconnected components. You need to demonstrate that you understand how your specific module (e.g., a SLAM pipeline or an RL policy) interacts with the rest of the robot, including sensors, actuators, and safety critical modules.

Collaborative Problem Solving – Our team operates in a highly collaborative environment. We want to see how you brainstorm, how you receive constructive feedback during technical discussions, and how you communicate complex technical concepts to engineers outside your immediate domain.

The interview process at Foundation Robotics Labs is designed to evaluate your technical excellence, practical coding skills, and alignment with our mission. We aim to make the process transparent, rigorous, and highly technical, reflecting the actual day-to-day challenges you will solve on the job.

The process begins with an initial technical conversation with a recruiter, followed by a deeply technical screen focused on coding and system design. If you pass this stage, you will move to a comprehensive onsite loop. This loop features a mix of deep-dive technical discussions, hands-on coding challenges, and behavioral conversations designed to assess how you work within a fast-paced hardware-software team.

The timeline shown above represents our standard interview pipeline, which typically takes three to four weeks from the initial screen to the final offer. We move quickly for exceptional candidates, and your recruiter will keep you updated at every stage of the process. While the technical core remains consistent, we tailor the specific focus of your onsite rounds to match the track you are interviewing for—whether that is SLAM, RL & WBC, VLM & VLA, or World Models.

Physical Intelligence & World Models

For candidates focusing on VLM & VLA or World Models, this evaluation area probes your ability to build models that understand physical reality. We want to see how you design architectures that can predict the future, reason about physical properties, and translate natural language instructions into concrete physical actions.

Be ready to go over:

• Multimodal Tokenization – How to efficiently represent images, text, and continuous robot state/action vectors in a unified latent space.
• Predictive World Modeling – Designing self-supervised objectives that allow models to learn intuitive physics, object permanence, and spatial relationships.
• Inference Optimization – Techniques such as quantization, pruning, and speculative decoding to run large models within tight robot control loops.
• Advanced concepts (less common) – Diffusion policies for action generation, multi-task imitation learning at scale, and cross-embodiment data pre-training.

Example questions or scenarios:

• "Design an end-to-end architecture that takes a natural language instruction and high-resolution camera feeds to generate joint velocity commands for a 7-DOF robot arm."
• "How would you train a world model to predict the visual and physical consequences of a robot interacting with highly deformable objects like clothing or liquids?"

Reinforcement Learning & Whole-Body Control (WBC)

This area is critical for the RL & WBC track. We assess your ability to design control strategies that keep our humanoid platforms balanced, agile, and safe. You must demonstrate a deep understanding of both modern model-free reinforcement learning and classical model-based control theory.

Be ready to go over:

• Quadratic Programming (QP) Formulations – Setting up rigid-body dynamics, contact constraints, and task hierarchies for real-time control.
• Sim-to-Real Transfer – Utilizing domain randomization, system identification, and dynamics randomization to ensure policies transfer successfully to physical robots.
• Policy Architectures – Designing neural network architectures that output smooth, safe actions while incorporating physical priors.
• Advanced concepts (less common) – Safe RL with control barrier functions (CBFs), trajectory optimization under contact-implicit dynamics, and hierarchical RL.

Example questions or scenarios:

• "Formulate a Whole-Body Control framework for a bipedal robot standing on a moving platform. What are your primary objectives, and how do you handle sudden external perturbations?"
• "Explain how you would design a reward function and training curriculum to teach a humanoid robot to run, ensuring the resulting policy does not damage the physical joints."

Spatial AI & SLAM

For SLAM candidates, this section evaluates your expertise in helping our robots build precise maps and track their location in real-time. We look for a deep understanding of geometry, optimization, and sensor characteristics.

Be ready to go over:

• Sensor Fusion – Tight vs. loose coupling of cameras, LiDARs, IMUs, and wheel odometry.
• Non-Linear Optimization – Sparse BA (Bundle Adjustment), factor graphs, marginalization, and sliding window filter formulations.
• Loop Closure & Relocalization – Place recognition algorithms, robust estimation (RANSAC, M-estimators), and global consistency.
• Advanced concepts (less common) – Semantic SLAM, neural radiance fields (NeRFs) or 3D Gaussian Splatting for real-time spatial representations, and dynamic obstacle filtering.

Example questions or scenarios:

• "Walk through the mathematical formulation of a visual-inertial odometry (VIO) system. How do you estimate and track the IMU biases over time?"
• "How would you design a SLAM system for a warehouse robot that must operate reliably in environments where shelving and objects are constantly being rearranged?"

High-Performance Robotics Systems

Regardless of your track, you must be a strong systems engineer. This round evaluates your ability to write clean, high-performance, and thread-safe code that can interface directly with low-level hardware and real-time operating systems.

Be ready to go over:

• Real-Time C++ – Avoiding allocations, understanding lock-free programming, and managing thread priorities under RT-preempt kernels.
• Concurrency & Parallelism – Designing efficient multi-threaded pipelines, managing race conditions, and utilizing modern C++ concurrency primitives.
• Memory Management – Cache-friendly data structures, custom allocators, and hardware-accelerated memory copy operations.
• Advanced concepts (less common) – Profiling with tools like perf or Valgrind, writing custom CUDA kernels, and optimizing serialization formats for high-bandwidth sensor streams.

Example questions or scenarios:

• "You have a camera thread producing images at 60Hz and a perception thread consuming them at 15Hz. Implement a thread-safe frame buffer that drops old frames without blocking the producer."
• "How would you optimize a large-scale matrix multiplication pipeline in C++ to leverage SIMD instructions and maximize L1/L2 cache hits?"