Skydio·Machine Learning Engineer·Take-home Assignment·Senior

Senior

Jun 2026

Summary

Take-home assignment for an ML Engineer role at Skydio, focused entirely on building an image segmentation pipeline from scratch. Pretty involved for a take-home, covering everything from data loading to inference scripts and a written explanation of your choices.

Questions Asked (1)

Build a complete image segmentation pipeline: data loading and augmentation, an encoder-decoder model (like U-Net), a training script with appropriate loss functions, evaluation metrics including IoU, an inference script that outputs mask images, and a short writeup on your design decisions and trade-offs.

Technical Trade-offsSystem DesignAlgorithms & Data Structures

Author's notes

This is a lot to ask for a take-home.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the problem context—dataset, classes, and deployment constraints—then walk through the pipeline end-to-end, highlighting key design choices and trade-offs at each stage. Emphasize modularity, reproducibility, and how you would validate and iterate on the system.

Pro tip: Show awareness of real-world constraints like class imbalance, limited labeled data, and inference latency; mention specific techniques (e.g., Dice loss, test-time augmentation) and how they impact performance. Also, briefly discuss how you would monitor and maintain the model post-deployment.

1. Clarify requirements and constraints

Ask about dataset size, class distribution, input resolution, latency/throughput needs, and deployment target (e.g., edge device). This shapes architecture, augmentation, and optimization choices.

2. Design data pipeline

Describe loading, preprocessing, and augmentation (e.g., random flips, rotations, color jitter). Discuss handling class imbalance via weighted sampling or loss weighting, and use of efficient data loaders with caching.

3. Model architecture and training

Propose a U-Net-like encoder-decoder with skip connections. Explain encoder choice (e.g., ResNet, EfficientNet) and decoder design. Detail loss functions (e.g., cross-entropy + Dice) and optimizer/scheduler. Mention mixed precision and gradient accumulation for memory efficiency.

4. Evaluation and metrics

Define metrics: IoU (per class and mean), Dice coefficient, pixel accuracy, and boundary F1. Discuss validation strategy (e.g., k-fold, hold-out) and how to handle small datasets. Include visualization of predictions vs. ground truth.

5. Inference and deployment

Outline inference script: load model, preprocess input, run forward pass, post-process (e.g., argmax, thresholding), and save masks. Discuss optimizations like quantization, pruning, or TensorRT for latency. Mention test-time augmentation if beneficial.

Key Points to Mention

Choice of loss function: combine cross-entropy with Dice loss to handle class imbalance and improve boundary segmentation.
Data augmentation techniques: geometric (flips, rotations, scaling) and photometric (brightness, contrast) to improve generalization.
Encoder-decoder architecture: use pretrained encoder (e.g., ResNet) for feature extraction, skip connections to preserve spatial details.
Evaluation metrics: IoU (Jaccard index) per class and mean IoU, Dice coefficient, and pixel accuracy; discuss limitations and when to use each.
Training tricks: learning rate scheduling (e.g., cosine annealing), early stopping, mixed precision training, and gradient clipping.
Inference optimizations: model quantization, ONNX/TensorRT conversion, and batch processing for throughput; consider edge deployment constraints.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.