Artificial Intelligence and Machine Learning Expertise Questions

Articulate deep expertise in one or more artificial intelligence and machine learning domains relevant to the role. Cover areas such as neural network architecture design, deep learning systems, natural language processing and large language models, generative artificial intelligence, computer vision, reinforcement learning, and full stack machine learning systems. Describe specific projects and products, datasets and data pipelines, model selection and evaluation strategies, performance metrics, experimentation and ablation studies, chosen frameworks and tooling, productionization and deployment experience, scalability and inference optimization, monitoring and maintenance practices, and contributions to model interpretability and bias mitigation. Explain the measurable impact of your work on product outcomes or research goals, trade offs you managed, and how your specialization aligns to the hiring organization needs.

MediumTechnical

80 practiced

Compare model compression techniques: pruning, quantization, knowledge distillation, and low-rank factorization. For a mobile deployment that supports 8-bit integer inference, propose an end-to-end compression pipeline to achieve target latency and size while keeping accuracy loss ≤2%. Include evaluation steps to ensure robustness across real-world inputs.

HardTechnical

67 practiced

In PyTorch, implement a training loop pattern that uses gradient checkpointing (activation checkpointing) and gradient accumulation to train transformer models that exceed a single GPU's memory. Provide a code sketch showing: model checkpointing usage, accumulation steps logic, optimizer step timing, and considerations for mixed precision (AMP). Explain memory-compute trade-offs and any pitfalls.

EasyTechnical

68 practiced

Compare pretraining and fine-tuning strategies for large models. Provide examples from CV (ImageNet pretraining) and NLP (BERT/GPT pretraining). When would you use feature-based transfer (frozen pretrained features) versus full fine-tuning? Discuss compute, data efficiency, and inference-time trade-offs.

MediumTechnical

81 practiced

Implement in Python a BalancedBatchSampler class that, given a list of labels and desired batch_size, yields minibatch indices where classes are sampled to balance class frequencies. The sampler should handle classes with fewer examples than needed per batch by oversampling or by cycling. Include a simple API and discuss time/space complexity and reproducibility (random seed).

HardTechnical

74 practiced

Design a fairness evaluation and mitigation plan for a hiring-assistant model where sensitive attributes (e.g., gender, race) are partially missing and historical selection bias exists. Define metrics that capture intersectional fairness, propose mitigation strategies (reweighting, adversarial debiasing, constrained optimization), and describe auditing, dataset augmentation, and governance processes to ensure ongoing fairness monitoring.

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