Artificial Intelligence and Machine Learning Progression Questions
Personal career narrative focused on progression within artificial intelligence and machine learning domains toward senior or staff level roles. Candidates should highlight domain specific milestones such as research contributions, production AI systems designed or architected, scale and complexity of models and pipelines, leadership of ML initiatives, cross functional influence on product or infrastructure, publications or patents if applicable, and how technical depth and organizational impact grew over time. Include concrete examples of projects, measures of system performance or business impact, and how domain expertise informs readiness for advanced technical leadership roles.
MediumTechnical
0 practiced
Design an automated data validation and quality-assurance layer for ML pipelines to catch schema changes, null rate spikes, distribution shifts, outliers, and label leakage. Specify checks to run at ingest, pre-training, and post-training, tooling choices (e.g., Great Expectations), and how to decide whether to fail-fast versus send warnings.
EasyTechnical
0 practiced
Implement in Python a function rolling_mean(stream, window_size) that consumes an iterable/iterator of numeric values and yields the rolling mean for each position in a streaming manner. Use O(1) additional memory beyond storing the window values and provide an example: input [1,2,3,4,5], window_size=3 => outputs [1.0,1.5,2.0,3.0,4.0]. Include the function signature and brief explanation of approach.
MediumTechnical
0 practiced
How would you design a production ML data pipeline to be idempotent, resilient to upstream failures, and provide end-to-end lineage for debugging and audits? Discuss patterns (checkpointing, watermarking, retries, idempotent transforms), tooling choices (Airflow, Kafka, Spark, Flink), and how you would expose lineage and provenance to engineering and compliance stakeholders.
MediumTechnical
0 practiced
Design a scalable human-in-the-loop labeling system for image annotation that supports worker management, quality control, consensus algorithms, and active learning to prioritize samples. Describe APIs for annotation, mechanisms to route ambiguous examples for expert review, and how labeled data flows back into training pipelines with QA checks.
MediumSystem Design
0 practiced
A mid-sized company must choose orchestration for varied ML workloads: scheduled batch pipelines, streaming transformations, ad-hoc model training, and model serving. Compare options like Airflow, Kubernetes-native operators (Argo/Knative), Kubeflow Pipelines, and serverless workflows. Recommend an approach balancing developer experience, reliability, reproducibility, and operational cost.
Unlock Full Question Bank
Get access to hundreds of Artificial Intelligence and Machine Learning Progression interview questions and detailed answers.
