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Altos Labs Machine Learning Engineer Interview Questions

The questions to prepare for a Altos Labs Machine Learning Engineer interview. Questions from real interview reports rank first. Updated weekly.

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1
Machine LearningStart here. 8 questions · ~64 min
Self-Supervised Objective DesignHard

Tests ability to design self-supervised objectives under weak or missing supervision for ML in biotech settings.

Neural NetworksUnsupervised LearningDeep LearningAltos Labs
Modeling Noisy DataMedium

Tests robustness techniques and decision-making when real-world data quality diverges from expectations.

Hyperparameter TuningFeature EngineeringRegularizationAltos Labs
Mitigating Catastrophic ForgettingHard

Tests strategies for stable fine-tuning and retention when adapting foundation models to specialized biotech data.

Hyperparameter TuningRegularizationDeep LearningAltos Labs
Multimodal Cellular State PredictionHard

Tests multimodal modeling design for predicting cellular state from images and genomic signals relevant to cell rejuvenation.

Neural NetworksFeature EngineeringDeep LearningAltos Labs
Handling Modality Loss ImbalanceHard

Tests ability to diagnose and fix multimodal training pathologies like loss imbalance and representation collapse.

Hyperparameter TuningRegularizationDeep LearningAltos Labs
GNN vs Transformer TradeoffsMedium

Tests understanding of when to use GNNs vs transformers for relational structure in biological data.

Neural NetworksFeature EngineeringDeep LearningAltos Labs
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2
More topics2 questions · ~16 min
Petabyte-Scale Image Data LoadingHard

Tests systems thinking for high-throughput data pipelines that keep GPUs utilized for large-scale cellular imaging.

InfrastructureBatch ProcessingQualityAltos Labs
Memory-Efficient Custom Attention in PyTorchHard

Tests coding ability to implement optimized attention mechanisms with careful memory management in PyTorch.

ArraysGreedyMatrixAltos Labs

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