Headspace ML Engineer Interview Questions Asked in Recent Interviews

Aletha Payawal
Written by Aletha Payawal
Wei
Reviewed by Wei
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Introduction

Headspace’s engineering interview process runs three to four rounds, and candidates report timelines exceeding three months depending on scheduling. The process screens for experience building and deploying ML systems in personalization and conversational AI, reflecting Headspace’s focus on recommendation engines and language-based mental health applications. Technical screening is outsourced to Karat, a third-party live interviewing service, before candidates ever speak with a Headspace engineer.

Interview Topics

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Data Structures & Algorithms
(176)
Machine Learning
(120)
Probability
(62)
Statistics
(40)
AI & Agentic Systems
(18)

The Headspace ML Engineer Interview Process

1

Recruiter Screen

A Headspace recruiter reaches out for an initial phone call covering background, role expectations, and logistics. This stage is conversational and screens for basic fit before any technical evaluation begins. Candidates report the recruiter is typically transparent about the role, including details that may differ from the job posting.

Based on candidate reports

Recruiter Screen
2

Karat Technical Screen

Headspace routes all technical screening through Karat, a third-party live interviewing service staffed by professional interviewers rather than Headspace engineers. The session runs approximately one hour and covers coding in a shared IDE along with system design questions. One candidate noted the Karat interviewer was “only focused on moving quickly through the process,” so expect a brisk pace with limited back-and-forth.

Based on candidate reports

Karat Technical Screen
3

Coding Panel

Candidates who clear Karat move into a coding panel with Headspace engineers, typically two back-to-back sessions scheduled on the same day. Questions land in the medium-to-hard range on the difficulty scale, with an emphasis on data structures and algorithms. Multiple candidates describe the sessions as intensive, with one noting the overall process feels “laborious” given the sequential scheduling.

Based on candidate reports

Coding Panel
4

Cross-Functional Panel

The cross-functional panel brings in four members from across the engineering organization and runs as a multi-person interview loop. This stage evaluates collaboration style, communication, and how candidates reason through ambiguous problems in a team context. Candidates report this round is distinct from the technical sessions and focuses less on code and more on how you work.

Based on candidate reports

Cross-Functional Panel
5

Case Study

A case study round follows the panel, requiring candidates to work through a realistic problem relevant to the role. For ML engineers, this stage assesses applied modeling judgment, including how candidates define problems, select approaches, and reason about tradeoffs in production settings. Headspace’s flow places this after the cross-functional panel, making it one of the final evaluation gates before a hiring manager decision.

Based on candidate reports

Case Study
6

Hiring Manager Close

The final stage is a closing call with the hiring manager or a senior leader, used to align on fit and level before a decision is made. This is not a technical round. Candidates report the overall process from recruiter screen to this stage regularly stretches beyond three months.

Based on candidate reports

Hiring Manager Close

Challenge

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Featured Interview Question at Headspace

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Headspace ML Engineer Interview Questions

QuestionTopicDifficulty
Data Structures & Algorithms
Easy

Given two sorted lists, write a function to merge them into one sorted list.

Bonus: What’s the time complexity?

Example:

Input:

list1 = [1,2,5]
list2 = [2,4,6]

Output:

def merge_list(list1,list2) -> [1,2,2,4,5,6]
Machine Learning
Easy
Statistics
Easy

470+ more questions with detailed answer frameworks inside the guide

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