Higher People Machine Learning Engineer Interview: Questions, Experience & Prep (2026)

Higher People currently has 42 open Machine Learning Engineer roles listed on knok jobradar, making it one of the more active ML hirers right now. Candidates report the interview process typically runs three to four rounds, covering ML fundamentals, coding problems, and a system design or product case discussion. The company's work spans people analytics and talent technology, so interviewers often frame ML problems around hiring signals, candidate matching, and NLP on text-heavy data.

Expect a process that is structured but conversational. Interviewers want to see clear thinking under ambiguity, not just correct answers to textbook questions. The 803 Machine Learning Engineer roles open across India on knok jobradar (as of mid-2026) show strong market demand, with Bangalore leading at 165 openings and Delhi at 50. Higher People's 42 roles represent a meaningful share of active hiring in this space right now.

These questions come up repeatedly in ML Engineer interviews at companies in the people-tech space, and candidates report seeing many of them at Higher People.

1. Walk me through how you would build a resume-to-job matching model from scratch.
2. How do you handle class imbalance when building a model to predict whether a candidate will accept an offer?
3. Explain the difference between precision and recall. Which matters more when screening candidates, and why?
4. You have a recommendation system that worked well in offline evaluation but is underperforming in production. What do you investigate first?
5. How would you detect and reduce bias in a hiring-related ML model?
6. Describe a time you had to choose between model accuracy and model explainability. How did you decide?
7. How do you evaluate an NLP model built to extract skills from job descriptions?
8. Walk me through your approach to feature engineering on a structured dataset about user behaviour.
9. How do you monitor a deployed model for data drift, and what actions do you take when you detect it?
10. Explain how you would A/B test a new ranking algorithm for job recommendations.
11. What is your approach to debugging a model whose performance has degraded after a data pipeline change?
12. How would you scale a batch prediction pipeline to handle a large volume of resumes nightly?

Q: Walk me through how you would build a resume-to-job matching model from scratch.

*Situation:* At my previous company, we had a large volume of applicants but no automated way to surface the most relevant candidates for a given job description.

*Task:* I was asked to prototype a matching model that could rank candidates by relevance before human reviewers looked at them.

*Action:* I started with data collection, pulling historical hiring decisions as weak labels. I used a pre-trained sentence transformer to encode both resumes and job descriptions into embeddings, then computed cosine similarity as a baseline. I then fine-tuned the model using positive pairs (hired candidates) and negative pairs (rejected candidates) via contrastive loss. I also added structured features like years of experience and required skills overlap. I evaluated using NDCG on a held-out set of past hiring decisions.

*Result:* The fine-tuned model improved ranking quality over the baseline, and reviewers reported spending less time skipping irrelevant profiles. The system moved into a pilot with one business unit.

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Q: How do you handle class imbalance when predicting candidate offer acceptance?

*Situation:* I worked on a model to predict whether a shortlisted candidate would accept an offer. The acceptance rate in our historical data was low, creating a heavily imbalanced dataset.

*Task:* My task was to build a model that did not simply predict 'rejection' for everyone and still show useful performance for the hiring team.

*Action:* I first established a baseline using class weights in the loss function to penalise misclassification of the minority class more heavily. I then experimented with SMOTE to oversample the minority class and compared it against undersampling the majority class. I evaluated each approach using the F1 score for the minority class rather than overall accuracy, since overall accuracy was misleading with this data.

*Result:* The class-weighted approach with careful threshold tuning gave the best business outcome. The hiring team could use the model's probability scores to prioritise follow-ups with candidates most likely to accept.

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Q: Describe a time you had to choose between model accuracy and explainability.

*Situation:* I was building a model to flag high-potential candidates for a client in a regulated industry. A gradient-boosted model gave the best performance on our evaluation set, but the client's compliance team required that every decision be explainable to candidates.

*Task:* I had to decide whether to use the more accurate black-box model or a simpler, interpretable one, and justify that choice to both the technical team and the client.

*Action:* I first tried SHAP values on the gradient-boosted model to see if post-hoc explanations would satisfy the compliance requirement. After reviewing with the client's team, they felt the SHAP output was still too complex for candidates to understand. I then trained a logistic regression model with careful feature engineering, which closed most of the performance gap while producing plain-language explanations.

*Result:* The logistic regression model was approved by compliance and deployed. The small drop in performance was accepted because it unlocked a client segment the company could not otherwise serve.

The STAR format is the most reliable structure for behavioural questions at Higher People and most product companies. State the Situation briefly, clarify your Task, spend the most time on your Action (the what and why of your choices), and close with a concrete Result.

For technical design questions, candidates report that a structured walk-through works well: restate the problem in your own words, clarify constraints, propose a simple baseline first, then layer in complexity. Interviewers typically want to see that you can ship something simple before you propose a complicated architecture.

For ML metrics questions, tie your answer to the business goal immediately. Saying 'I would use F1' is weaker than saying 'In a hiring context, false negatives mean missing good candidates, so recall on the positive class matters more, but precision matters too because reviewer time is limited. I would use F1 and also look at the precision-recall curve to choose a threshold.'

For bias and fairness questions, show that you know the difference between fairness definitions like demographic parity and equalised odds, and that you understand the trade-offs between them. Candidates who treat this as a purely technical problem without acknowledging the legal and ethical dimensions are typically marked down.

Candidates who have interviewed at Higher People typically describe interviewers as genuinely curious about how you think, not just whether you arrive at the right answer. A few patterns stand out.

Comfort with ambiguity. ML problems in people-tech rarely have a clean ground truth. Interviewers want to see that you ask clarifying questions, state your assumptions explicitly, and are comfortable saying 'it depends' when it actually does.

End-to-end ownership. The questions span data collection, modelling, evaluation, and deployment. Candidates who can speak confidently about the full pipeline, including monitoring and retraining, stand out over those who only know the modelling step.

Awareness of real-world constraints. Interviewers respond well to candidates who mention bias, fairness, latency, and data quality as natural parts of the problem, not as afterthoughts.

Clear communication. Because the work touches hiring decisions, you will likely interact with non-technical stakeholders. Interviewers at people-tech companies tend to value candidates who can explain ML concepts in plain language without losing precision.

Week 1: Strengthen fundamentals. Revisit supervised and unsupervised learning, loss functions, regularisation, and evaluation metrics. Pay extra attention to metrics used in ranking and recommendation tasks (NDCG, MAP, MRR) since these come up often in people-tech interviews. Practise explaining the bias-variance trade-off and overfitting in plain language.

Week 2: NLP and recommendation systems. Higher People's ML work likely involves text-heavy data. Review transformer-based embeddings, fine-tuning strategies, and cosine similarity for semantic search. Also review collaborative filtering, content-based filtering, and hybrid recommendation systems.

Week 3: ML system design. Practise designing end-to-end ML pipelines out loud. Pick problems like 'design a job recommendation system' or 'build a resume screening model' and walk through data collection, feature engineering, modelling, evaluation, deployment, and monitoring. Focus on stating trade-offs clearly.

Week 4: Coding and behavioural prep. Practise coding problems involving array manipulation, hash maps, and graph traversal at a comfortable pace. These are typically not highly competitive coding rounds, but clean and readable code matters. For behavioural questions, prepare three to four stories from your past work that cover a failed project, a cross-functional collaboration, and a time you improved a system in production.

With 42 open roles at Higher People and 803 ML Engineer openings across India right now, the market is active. knok checks 150+ job sites nightly, applies to jobs matching your resume, and messages HR for you, so you can focus your prep time on interviews rather than applications.

Skipping the baseline. Candidates sometimes jump straight to a complex deep learning solution without mentioning a simpler baseline first. Interviewers at Higher People typically want to see that you would validate a simple model before investing in complexity.

Treating evaluation as an afterthought. Saying 'I would check accuracy' without discussing why accuracy may be the wrong metric for imbalanced or high-stakes problems signals a gap. Always connect your metric choice to the business context.

Ignoring fairness. For a company in the people-tech space, ML fairness is not an optional topic. Candidates who cannot discuss bias in hiring models, or who dismiss it as a data problem alone, are typically not moved forward.

Over-engineering the system design. Proposing a highly distributed real-time inference system for a problem that could be solved with nightly batch scoring shows poor judgement. Start simple, state the trade-offs, and scale up only when there is a clear reason.

Vague results in STAR answers. Ending a story with 'the model performed better' without any supporting detail leaves interviewers with nothing to evaluate. Quantify where you can, and if numbers are not available, describe the business impact in concrete terms.

Not asking clarifying questions. Jumping into an answer without clarifying the problem scope is a common miss. Interviewers often leave problems intentionally open-ended to see if you will ask the right questions before solving.