Google Forward Deployed Engineering Interview Prep
This repository documents my preparation for the Google Forward Deployed Engineering (FDE) interview process. It serves as a structured collection of notes, timelines, strategies I employed, practice problems, and reflections across all major interview components.
📌 Overview
The Forward Deployed Engineering role blends software engineering, problem-solving, and customer-facing skills. This repo is organised to reflect the multi-dimensional nature of the interview process:
- Timeline & Recruiter Screening
- Data Structures & Algorithms (DSA)
- Vibe Coding (practical, collaborative coding)
- Googleyness (behavioral & culture fit)
- Agentic & Machine Learning System Design
⏳ Timeline & Recruiter Screening
Focus: Mentioning of past experiences, RRK and Why Factor?
Contents:
- What interests you in this role?
- Tell me about your past experiences? (Prepare 1 Agentic Model & 1 Classical ML Example)
- 30 - 45 mins where you should ask questions more around the Team and the recruiter too
- What are you pursuing outside of work?
- End-To-End Interview Process takes 6-8 Weeks including self-preparation
🧠 Data Structures & Algorithms (DSA)
Focus: Problem-solving under constraints, code efficiency, and clarity.
Contents:
- Core topics (arrays, trees, graphs, DP, etc.)
- Practice problems with solutions
- Patterns and heuristics
- Time/space complexity breakdowns
- Mock interview notes
Goal: Build strong intuition and the ability to communicate solutions clearly.
💻 Vibe Coding
Focus: Real-world coding in a collaborative, fast-paced environment.
Contents:
- End-to-end coding exercises
- Code readability and structure
- Debugging strategies
- Working with ambiguous requirements
- Communication during live coding
Goal: Demonstrate practical engineering skills and adaptability.
🤝 Googleyness Interview
Focus: Behavioural traits aligned with Google's values.
Contents:
- STAR method responses
- Past experience breakdowns
- Leadership, ownership, and teamwork examples
- Handling ambiguity and failure
- Reflection logs
Goal: Show thoughtful decision-making, humility, and impact.
🤖 Agentic & ML Design
Focus: Designing intelligent, scalable systems with ML components.
Contents:
- System design fundamentals
- ML system architecture
- Agent-based workflows
- Trade-offs and scalability considerations
- Case studies and mock designs
Goal: Communicate high-level design clearly while reasoning about complexity and constraints.
📅 How to Use this Repo:
A structured way to cover all topics and be confident in the interview:
- Understand various DSA Python Patterns using the python_dsa_patterns.ipynb notebook - 80% of DSA is covered there
- Understand how Agentic System Design is built using the ml_agentic_systems_guide attached
- Practise the Problem Set using the dsa_problem_set file
🛠️ Tools & Resources
- Coding platforms (NeetCode - It will cost you $100 but definitely worth it)
- System design references
- ML papers and blogs
- Mock interview platforms
📝 Notes & Reflections
This section captures:
- Lessons learned
- Mistakes and improvements
- Interview experiences (if applicable)
- Iterations on answers and approaches
🚀 Purpose
This repository is both:
- A personal knowledge base
- A transparent record of my preparation process
⚠️ Disclaimer
This repository is based on personal preparation and experiences. It does not contain proprietary or confidential information about any company or interview process.
📬 Contributions
This is a personal repo, but suggestions or discussions are always welcome!