HealthTech Startup Interview Experience For ML Engineer

I’m a prefinal year student pursuing a Bachelor’s in Computer Science with a specialization in AIML at LNCT, Bhopal. My passion for leveraging machine learning to improve healthcare outcomes led me to apply for a future ML Engineer role at a promising health tech startup.

Job Application Process

I found the internship position through our university’s career services portal. The role appealed to me because it involved working on predictive models for patient care, which aligns with my academic projects and career aspirations. I submitted my resume along with a personal statement that detailed my relevant coursework and a project that used machine learning to analyze clinical data.

Interview Format and Stages

The interview was structured to accommodate my academic schedule and consisted of several stages spread over two weeks:

• Initial Phone Screen: A brief discussion with HR to understand my availability post-graduation and to explain the role’s impact on the startup.
• Technical Phone Interview: A one-hour session with the lead ML Engineer to evaluate my understanding of machine learning fundamentals.

Virtual On-site Day:

• Technical Discussion: Focused on my technical skills and project experiences.
• Coding Challenge: Involved solving a problem relevant to their current research.
• Behavioural Interview: Assessed my soft skills and potential fit within the team.

Technical Interview

Machine Learning Foundations

Question: Describe how you would use regularization in a regression model.

Answer: I explained the concept of regularization to prevent overfitting, discussing both L1 and L2 regularization and their impact on model complexity.

Project Discussion

Project Review: I talked about my recent project on developing a machine-learning model to predict hospital readmissions. This involved dealing with imbalanced datasets, which was particularly relevant to the startup’s focus.

Coding Challenge

Problem: Write Python code to implement a decision tree classifier from scratch.

Approach and Solution: I described my step-by-step approach, focusing on how to handle data splits and purity calculations.

Behavioural Interview

• Scenario: What would you do if you disagreed with a team leader on the direction of a project?
• Response: I shared an experience where I used data to support my viewpoint, leading to a constructive discussion that ultimately benefited the project.

HR Round

We discussed potential starting dates after graduation, and they were flexible given my academic commitments. The conversation also covered typical startup benefits like stock options and the opportunity for rapid advancement.

Post-Interview

I was informed of a tentative offer, contingent on my final year grades and a formal review closer to my graduation date.

Tips for Future Candidates:

• Preparation: Focus on understanding the practical applications of theoretical concepts. Engage in projects that solve real-world problems.
• Interview: Be honest about your experiences and ready to discuss both successes and challenges you’ve faced. Show enthusiasm for the role and the startup’s mission.