To succeed in the Machine Learning Engineer interviews, you must demonstrate deep competence across several core technical and behavioral domains.
Computer Vision and Sensor Fusion
Given the nature of Anduril’s autonomous systems, computer vision is often a primary focus. You will be evaluated on your ability to process, analyze, and extract actionable intelligence from various data modalities, including electro-optical (EO), infrared (IR), and radar sensors. Strong performance here means showing you can build robust models that handle occlusion, varying lighting conditions, and extreme distances.
Be ready to go over:
- Object detection and tracking – Architectures like YOLO, Faster R-CNN, and SORT/DeepSORT.
- Sensor fusion techniques – Combining data from multiple disparate sensors to create a single, reliable track.
- Data augmentation – Strategies for training robust models when real-world labeled data for specific threat profiles is scarce.
- Advanced concepts (less common) – 3D point cloud processing, multi-agent reinforcement learning for swarm dynamics.
Example questions or scenarios:
- "How would you design an object detection pipeline to identify small drones against a cluttered background using both IR and EO cameras?"
- "Explain how you would handle track continuity if an object temporarily disappears behind a physical obstruction."
- "Walk me through your approach to generating synthetic training data for a rare vehicle type."
Model Optimization and Edge Deployment
Models at Anduril Industries must run efficiently on hardware like NVIDIA Jetson or custom embedded systems. Interviewers will test your ability to take a heavy, research-grade PyTorch model and strip it down for real-time inference without sacrificing critical accuracy. A strong candidate will speak fluently about hardware constraints and inference engines.
Be ready to go over:
- Quantization and pruning – Techniques for reducing model size and computational requirements (e.g., FP16, INT8).
- Inference frameworks – Experience with TensorRT, ONNX, or OpenVINO.
- Memory management – Understanding the bottlenecks of deploying ML models on devices with limited RAM and thermal constraints.
- Advanced concepts (less common) – Custom CUDA kernel writing, hardware-in-the-loop testing.
Example questions or scenarios:
- "Walk me through the steps you would take to convert a PyTorch model into a TensorRT engine for deployment on an edge device."
- "If your deployed model is dropping frames during inference, how do you diagnose and resolve the bottleneck?"
- "Discuss the trade-offs between post-training quantization and quantization-aware training in a mission-critical application."
Core Software Engineering
At Anduril, ML Engineers are expected to be stellar software engineers first and foremost. You will be evaluated on your ability to write clean, modular, and highly performant code. Strong performance requires demonstrating that you can build scalable data pipelines, integrate ML models into larger software ecosystems, and write code that won't fail in the field.
Be ready to go over:
- Data structures and algorithms – Standard coding challenges focusing on arrays, graphs, and optimization.
- Systems programming – Proficiency in C++ for performance-critical components and Python for training/scripting.
- Concurrency and multi-threading – Designing systems that can handle multiple high-bandwidth sensor streams simultaneously.
- Advanced concepts (less common) – Real-time operating systems (RTOS), low-level networking protocols.
Example questions or scenarios:
- "Write a function in C++ to efficiently merge multiple overlapping bounding boxes from different sensor feeds."
- "How do you structure your Python training code to ensure reproducibility and easy tracking of experiments?"
- "Design a multi-threaded data ingestion pipeline that processes camera feeds at 60 FPS without dropping frames."
Behavioral and Mission Alignment
Because the work directly impacts national security, interviewers deeply probe your motivations, resilience, and ability to work in a high-stakes environment. Strong performance in this area means providing concise, structured examples of past projects where you took extreme ownership, navigated ambiguity, and delivered tangible results.
Be ready to go over:
- Bias for action – Examples of times you pushed a project forward despite lacking perfect information or a complete job description.
- Cross-functional collaboration – How you communicate complex ML trade-offs to hardware engineers or product managers.
- Handling failure – Discussing a time a model failed in production and the exact steps you took to remediate it.
- Advanced concepts (less common) – Navigating ethical considerations in defense tech, mentoring junior engineers under tight deadlines.
Example questions or scenarios:
- "Tell me about a time you had to build a system from scratch with very vague initial requirements."
- "Describe a situation where you had to compromise on model accuracy to meet strict latency or hardware constraints."
- "Why do you want to work in the defense technology sector, and why Anduril specifically?"
