Question 1

How many years of machine learning experience do you have?

Accepted Answer

This is a knockout question used to screen candidates. The suggested rule for this question is: 'Below minimum = Knockout'. The question type is MCQ: 0-1 / 1-3 / 3-5 / 5+.

Question 2

Are you proficient in Python for ML?

Accepted Answer

This is a knockout question used to screen candidates. The suggested rule for this question is: 'No = Hard Knockout'. The question type is Yes / No.

Question 3

Have you worked with ML frameworks?

Accepted Answer

This is a knockout question used to screen candidates. The suggested rule for this question is: 'None = Hard Knockout'. The question type is MCQ: TensorFlow / PyTorch / Scikit-learn / None.

Question 4

Have you trained and deployed ML models in production?

Accepted Answer

This is a knockout question used to screen candidates. The suggested rule for this question is: 'No = Knockout for production ML roles'. The question type is Yes / No.

Question 5

Have you worked with large datasets for model training?

Accepted Answer

This is a knockout question used to screen candidates. The suggested rule for this question is: 'No = Red flag'. The question type is Yes / No.

Question 6

Have you worked with NLP models or text data?

Accepted Answer

This is a knockout question used to screen candidates. The suggested rule for this question is: 'No = Knockout for NLP roles'. The question type is Yes / No.

Question 7

Have you worked with computer vision models?

Accepted Answer

This is a knockout question used to screen candidates. The suggested rule for this question is: 'No = Knockout for vision roles'. The question type is Yes / No.

Question 8

Have you used cloud ML platforms? (AWS SageMaker, GCP Vertex, Azure ML)

Accepted Answer

This is a knockout question used to screen candidates. The suggested rule for this question is: 'No = Red flag for cloud-first teams'. The question type is Yes / No.

Question 9

Have you worked with LLMs or generative AI models?

Accepted Answer

This is a knockout question used to screen candidates. The suggested rule for this question is: 'No = Knockout for GenAI roles'. The question type is Yes / No.

Question 10

Have you done feature engineering and data preprocessing?

Accepted Answer

This is a knockout question used to screen candidates. The suggested rule for this question is: 'No = Knockout'. The question type is Yes / No.

Question 11

Have you used MLflow or similar tools for experiment tracking?

Accepted Answer

This is a knockout question used to screen candidates. The suggested rule for this question is: 'No = Red flag for structured ML teams'. The question type is Yes / No.

Question 12

Have you worked with vector databases? (Pinecone, Weaviate, FAISS)

Accepted Answer

This is a knockout question used to screen candidates. The suggested rule for this question is: 'No = Knockout for RAG/LLM roles'. The question type is Yes / No.

Question 13

Have you built ML pipelines or workflows? (Airflow, Kubeflow)

Accepted Answer

This is a knockout question used to screen candidates. The suggested rule for this question is: 'No = Knockout for MLOps roles'. The question type is Yes / No.

Question 14

Have you worked with SQL or big data tools? (Spark, BigQuery)

Accepted Answer

This is a knockout question used to screen candidates. The suggested rule for this question is: 'No = Red flag'. The question type is Yes / No.

Question 15

Have you evaluated model performance using standard metrics?

Accepted Answer

This is a knockout question used to screen candidates. The suggested rule for this question is: 'No = Knockout'. The question type is Yes / No.

Question 16

Have you published research or contributed to open-source ML projects?

Accepted Answer

This is a knockout question used to screen candidates. The suggested rule for this question is: 'No = Red flag for research-heavy roles'. The question type is Yes / No.

Question 17

Do you have a GitHub profile or project portfolio to share?

Accepted Answer

This is a knockout question used to screen candidates. The suggested rule for this question is: 'No = Red flag'. The question type is Yes / No.

Question 18

What is your expected salary range?

Accepted Answer

This is a knockout question used to screen candidates. The suggested rule for this question is: 'Out of budget = Knockout'. The question type is MCQ: Range bands.

Question 19

What is your current notice period?

Accepted Answer

This is a knockout question used to screen candidates. The suggested rule for this question is: 'Mismatch = Knockout'. The question type is MCQ: Immediate / 2 weeks / 1 month / 2+ months.

Question 20

Are you available for an interview within the next 7 days?

Accepted Answer

This is a knockout question used to screen candidates. The suggested rule for this question is: 'No = Deprioritize'. The question type is Yes / No.

Technical Screening Questions for Machine Learning Engineers

20 Knockout Questions for Machine Learning Engineers

Automate Your ML Screening

#	Question	Type	Knockout Rule
1	How many years of machine learning experience do you have?	MCQ: 0-1 / 1-3 / 3-5 / 5+	Below minimum = Knockout
2	Are you proficient in Python for ML?	Yes / No	No = Hard Knockout
3	Have you worked with ML frameworks?	MCQ: TensorFlow / PyTorch / Scikit-learn / None	None = Hard Knockout
4	Have you trained and deployed ML models in production?	Yes / No	No = Knockout for production ML roles
5	Have you worked with large datasets for model training?	Yes / No	No = Red flag
6	Have you worked with NLP models or text data?	Yes / No	No = Knockout for NLP roles
7	Have you worked with computer vision models?	Yes / No	No = Knockout for vision roles
8	Have you used cloud ML platforms? (AWS SageMaker, GCP Vertex, Azure ML)	Yes / No	No = Red flag for cloud-first teams
9	Have you worked with LLMs or generative AI models?	Yes / No	No = Knockout for GenAI roles
10	Have you done feature engineering and data preprocessing?	Yes / No	No = Knockout
11	Have you used MLflow or similar tools for experiment tracking?	Yes / No	No = Red flag for structured ML teams
12	Have you worked with vector databases? (Pinecone, Weaviate, FAISS)	Yes / No	No = Knockout for RAG/LLM roles
13	Have you built ML pipelines or workflows? (Airflow, Kubeflow)	Yes / No	No = Knockout for MLOps roles
14	Have you worked with SQL or big data tools? (Spark, BigQuery)	Yes / No	No = Red flag
15	Have you evaluated model performance using standard metrics?	Yes / No	No = Knockout
16	Have you published research or contributed to open-source ML projects?	Yes / No	No = Red flag for research-heavy roles
17	Do you have a GitHub profile or project portfolio to share?	Yes / No	No = Red flag
18	What is your expected salary range?	MCQ: Range bands	Out of budget = Knockout
19	What is your current notice period?	MCQ: Immediate / 2 weeks / 1 month / 2+ months	Mismatch = Knockout
20	Are you available for an interview within the next 7 days?	Yes / No	No = Deprioritize