Type · motivation

Growth · Software Engineer Interview Guide
Interview language: English
How to Pass the 01Health Software Engineer Interview in 2026
The 01Health DNA (TL;DR)
The 01Health Interview Loop
Your onsite loop will typically consist of 5 rounds.
- 1
Round 1
Recruiter ScreenMotivation, role fit, logistics. - 2
Round 2
Coding ScreenLeetCode-medium algorithmic problems under time pressure. - 3
Round 3
System DesignDistributed systems, trade-offs at scale, architecture under constraints. - 4
Round 4
Onsite CodingLeetCode-hard, debugging, code clarity, edge cases. - 5
Round 5
Behavioral / LeadershipPast evidence of ownership, influence, resolving conflict.
The Danger Zone: Top Reasons Candidates Fail
Based on our database of 01Health interview outcomes, avoid these common traps:
- Proposing a monolithic architecture without considering microservices or modularity for different functions (ingestion, processing, delivery).
- Not addressing potential data inconsistencies or reconciliation issues between different nodes/sensors.
- Failing to articulate the specific trade-offs made during the simplification process
- Handling overlapping time intervals incorrectly, leading to double-counting or missed entries.
Test Yourself: Real 01Health Questions
Three real prompts pulled from our database.
Type · algorithmic
Type · scalability
+ many more questions, signals, and worked examples
Sign up to unlock the full 01Health grading rubric
01Health Interview Question Bank
A sample from our database, grouped by round. Sign up to see the full set.
9 of 14 questions shown
Recruiter Screen
1- 1
Type · motivation
What specifically about 01Health's mission in the pharma space, and our focus on [mention a specific therapeutic area or technology if known], interests you as a software engineer?
Coding Screen
3- 2
Type · algorithmic
Given a dataset of patient treatment outcomes (e.g., drug efficacy, side effects) over time, design an algorithm to identify patients who are likely to experience a specific adverse event within the next 30 days. Assume data is time-series and potentially sparse. - 3
Type · data_structures
You have a large, sorted list of clinical trial participant IDs. You also have a stream of incoming events, each associated with a participant ID (e.g., a lab result, a doctor's note). Design a data structure to efficiently: 1. Check if a participant ID exists in the sorted list. 2. Add new events for a given participant ID. 3. Retrieve all events for a participant ID. - + 1 more questions in this round (sign up to unlock)
System Design
3- 4
Type · scalability
Design a system to manage and serve personalized drug information and dosage recommendations to millions of patients globally. Consider data ingestion from various sources (EHRs, wearables, patient input), real-time processing, and secure delivery. - 5
Type · reliability
Design a distributed system for real-time monitoring of clinical trial drug supply chains. The system should track inventory levels, temperature, and location of sensitive pharmaceuticals across multiple distribution centers and ensure data integrity. - + 1 more questions in this round (sign up to unlock)
Onsite Coding
4- 6
Type · hard_algorithmic
Implement a function to find the shortest path in a 3D grid representing a complex molecular structure, where movement is restricted by chemical bonds and energy potentials. The grid can be very large and sparse. - 7
Type · debugging
Here is a Python script that attempts to process patient EMR data to identify potential drug interactions. It's producing incorrect results and occasionally crashing. Debug and fix the code. - + 2 more questions in this round (sign up to unlock)
Behavioral / Leadership
3- 8
Type · ownership
Tell me about a time you encountered a significant technical challenge or bug in a production system that was impacting users (e.g., patients, doctors). What was your approach to diagnosing and resolving it, and what did you learn? - 9
Type · collaboration
At 01Health, we prioritize absolute clarity in our documentation. Tell me about a time you had to simplify a highly complex technical requirement or architectural trade-off for a non-technical stakeholder, such as a clinical researcher or regulatory lead. How did you ensure your explanation maintained the necessary technical precision without sacrificing readability? - + 1 more questions in this round (sign up to unlock)
Unlock all 14 01Health questions, free
No credit card. Every question with its framework, the grading signals interviewers score against, and a worked answer for each.
Interview tracks at 01Health
How 01Health's DNA translates across functions. Pick your role.
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Practice 01Health interviews end-to-end
01Health Mock Interview
Run a live mock interview with our AI interviewer using 01Health-style prompts. Get scored on structure, signal, and answer length - exactly how the real loop grades you.
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STAR Stories for 01Health Behavioral Rounds
Build a Story Bank of your past wins, mapped to the leadership signals 01Health interviewers grade on. Reuse them across every behavioral round.
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01Health Interview Prep Hub
The frameworks behind every 01Health round: CIRCLES for product sense, hypothesis-driven debugging for analytical, STAR for behavioral. Learn each one in 10 minutes.
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Interview Frameworks
CIRCLES, STAR, AARRR, RICE, MECE. The exact frameworks that make 01Health interviewers nod instead of frown. Step-by-step playbooks with the moves and the pitfalls.
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Sample answers
What a strong answer to these 01Health interview questions shows.
What specifically about 01Health's mission in the pharma space, and our focus on [mention a specific therapeutic area or technology if known], interests you as a software engineer?
A strong answer shows: Specific mentions of 01Health's products, research, or values.; Understanding of the intersection of technology and pharmaceuticals.; Enthusiasm for solving complex problems in a regulated industry..
Design an algorithm to detect anomalies in real-time drug dosage monitoring data. Anomalies could be sudden spikes, drops, or deviations from a patient's established baseline. The system needs to be low-latency.
A strong answer shows: Sliding window techniques for calculating baselines or detecting deviations.; Statistical methods (e.g., Z-score, IQR) adapted for streaming data.; Consideration of adaptive thresholds.; Discussion of latency requirements and trade-offs..