H2O.ai Interview Questions
& Process
Real candidates share what happened, how many rounds they had,
and how the experience turned out.
Based on 44 interview experiences · FREE TO READ
Which role are you interviewing for?
19 roles · 44 reportsCandidate interview experiences
First-hand accounts from people who interviewed at H2O.ai.
H2O.ai
UI Developer
Mid Average Neutral experience No offer 3 rounds
Interview process
Phone screen Technical screen Onsite
Interview formats
Coding Technical System Design
They asked me some coding questions, like basic to intermediate ones in Python or Java. Then we talked about my past ML/data science/engineering projects, what techniques I used, what challenges I ran into, and what trade-offs I made. There were also some theoretical ML/stats questions about algorithms and model evaluation. For senior roles, they might ask system design or architecture questions too.
Confirmed questions1 question
- What are the difficulties when deploying machine learning models into a production environment?
H2O.ai
Data Scientist
Intern Average Positive experience Accept offer 3 rounds
Interview process
Technical screen Technical screen Recruiter call
Interview formats
Technical Coding Behavioral
So first up there were two technical interviews with senior engineers, and after that I had an HR interview which was pretty easy. The first tech interview had some easy coding problems and questions about my past experiences. The second one focused on my ML projects and the theory behind them.
Confirmed questions4 questions
- Can you walk me through your ML projects?
- Are you able to handle some basic coding tasks?
- What are some key theoretical concepts you know?
H2O.ai
Data Scientist
Mid Average Neutral experience Decline offer 4 rounds
Interview process
Recruiter call Phone screen Technical screen Technical screen
Interview formats
Technical Behavioral
The interview process consists of 3 to 4 rounds: an initial HR chat, a meeting with the Hiring Manager, and interviews with two data scientists. The HR representative seemed unsure about regional benefits, which raised some concerns. The overall process involved considerable back-and-forth and wasn't the smoothest. During the data scientist interviews, expect a deep dive into your resume, so be ready to elaborate on your technical solutions.
Confirmed questions4 questions
- Can you elaborate on how RAG works?
- Tell me about the inner workings of an LLM.
- What experience do you have with LLMs?
H2O.ai
Software Engineer
Senior Difficult Negative experience No offer 5 rounds
Interview process
Recruiter call Technical screen Take home Onsite Panel Presentation Background check Offer
Interview formats
Technical Coding System Design Behavioral Presentation
This startup company doesn't have a rigid interview process. I interviewed for a Data Engineering role. It involved 5 rounds plus a coding challenge using H2O in Spark. I cleared all rounds with good feedback. They were flexible with interview timings, including weekends and late nights. Before an offer, I had a meeting with the CEO, who was quite rude and unprofessional, only available on Sundays. He didn't seem engaged during the interview and ultimately rejected me. The feedback was that I wasn't suitable because the role required both Data Engineering and customer support skills, and my experience was heavily skewed towards Data Engineering. This was disappointing as they initially emphasized the Data Engineering aspect.
Confirmed questions5 questions
- Questions related to Java
- Questions related to Scala
- Questions related to Spark
H2O.ai Interview Questions
Quoted word for word from H2O.ai interview reports.
“Write a function that takes an integer n and returns a string. For numbers divisible by 3, use 'Foo'. For numbers divisible by 5, use 'Bar'. For numbers divisible by both 3 and 5, use 'FooBar'. Otherwise, use the number itself. For example, if n=3, return '12Foo'. If n=6, return '12Foo4Bar6'.”
Read reports →“For time series analysis, would cross-validation be a suitable method?”
Read reports →“How do you ascertain feature importance in tree-based models?”
Read reports →“Could you please write a Python function in the Google Doc to find the second largest element in an array?”
Read reports →“How do you view a process in Arduino?”
Read report →“Can you fix this broken k8s cluster?”
Read report →“How might you leverage the output from the Word2Vec algorithm within Machine Learning Classification?”
Read report →“Can you provide a Java example of polymorphism?”
Read report →“For high cardinality categorical data, how would you approach dimension reduction?”
Read report →Formats, difficulty and experience
Across all 44 H2O.ai interview reports.