Scale Engineer

Software Engineer

Software EngineerL7Hard

Verily's L7 Software Engineer interview process is designed to assess a candidate's deep technical expertise, problem-solving abilities, system design skills, and leadership potential. The process is rigorous and aims to identify individuals who can drive complex projects, mentor junior engineers, and contribute significantly to Verily's innovative environment.

Rounds·4

Timeline·~4d

Experience·8 - 15 yrs

Comp band·US$180000 - US$250000

Interview time·225 min

Evaluation

What they measure.

  • Depth of technical knowledge in core computer science principles.
  • Proficiency in relevant programming languages (e.g., Python, Go, Java).
  • Ability to design, implement, and optimize complex algorithms.
  • Understanding of data structures and their trade-offs.

Preparation

How to prepare.

Tips

  1. Review fundamental computer science concepts: data structures, algorithms, operating systems, databases.
  2. Practice system design problems, focusing on scalability, reliability, and trade-offs.
  3. Prepare behavioral questions using the STAR method (Situation, Task, Action, Result).
  4. Research Verily's mission, products, and recent news to understand their business and technical challenges.
  5. Brush up on your preferred programming languages and be ready to write clean, efficient code.
  6. Understand distributed systems concepts like consensus, replication, and fault tolerance.
  7. Prepare to discuss your past projects in detail, highlighting your contributions and technical decisions.
  8. Consider practicing mock interviews with peers or mentors.

Study plan

Fig · Study plan — 04 phases

01 / 04
01

Phase 01 of 04

Data Structures and Algorithms

Weeks 1-2: Data Structures & Algorithms (LeetCode Medium/Hard)

Weeks 1-2: Focus on core data structures (arrays, linked lists, trees, graphs, hash tables) and algorithms (sorting, searching, dynamic programming, graph traversal). Practice implementing these in your primary language and analyze their time/space complexity. Solve LeetCode problems tagged 'Medium' and 'Hard'.

Questions

Commonly asked.

  • Design a system to manage patient health records, ensuring privacy and scalability.
  • How would you optimize a data pipeline processing millions of genomic sequences daily?
  • Describe a time you disagreed with a technical decision made by your team lead. How did you handle it?
  • What are the trade-offs between using a relational database and a NoSQL database for storing time-series biological data?
  • How do you approach mentoring junior engineers and fostering a collaborative team environment?
  • Imagine you need to build a real-time anomaly detection system for sensor data. Outline your approach.
  • Tell me about a challenging bug you encountered and how you debugged it.
  • How do you ensure the quality and reliability of software in a fast-paced environment?
  • What are your thoughts on microservices vs. monolithic architectures in the context of health tech?
  • Describe a situation where you had to influence stakeholders to adopt a new technical direction.

Locations

Regional differences.

Fig · Regions — 02 locations

01 / 02

Location

San Francisco Bay Area

Interview focus

Emphasis on practical application of advanced algorithms and data structures in biological contexts.Deeper dive into distributed systems and cloud-native architectures relevant to large-scale data analysis.Assessment of leadership and mentorship capabilities in driving technical initiatives.Understanding of regulatory compliance and data privacy in healthcare/life sciences.

Common questions

  • Discuss a time you had to influence a team to adopt a new technology. What was the outcome?
  • Describe a complex technical challenge you faced in a distributed system and how you resolved it.
  • How do you approach designing a scalable and reliable data processing pipeline for biological data?
  • Tell me about a project where you had to make significant architectural decisions. What was your thought process?
  • How do you stay updated with the latest advancements in software engineering and their potential application at Verily?

Tips

  • Familiarize yourself with common cloud platforms (GCP, AWS) and their services relevant to data science and machine learning.
  • Prepare to discuss specific examples of leading technical projects and mentoring junior engineers.
  • Research Verily's current projects and technologies to tailor your answers.
  • Be ready to articulate your understanding of scalability, fault tolerance, and performance optimization in large-scale systems.

Rounds

Round-by-round.

Expand a step for evaluation criteria, sample questions, and prep notes.

DSA

Coding questions at Verily.

Frequently reported on Verily loops

View all at Verily

Other guides

More at Verily.