eBay Staff Software Engineer | AI Platform | Interview Experience

Experience: 5+ Years
Base Salary: ₹54 LPA
Total Annual Compensation: ₹85 LPA

I interviewed for the Staff Software Engineer, AI Platform role at eBay. The interview covered DSA, system design, AI platform engineering, and technical leadership.

Here is what I was asked about in each round.

1. DSA

The DSA round focused mainly on backtracking and graph problems.

• Sudoku Solver: I was asked a backtracking problem based on solving a Sudoku puzzle, followed by some questions on the approach and possible edge cases.

• Graph and DFS: There were also graph problems involving Depth-First Search (DFS), with follow-up questions to explore the solution further.

The discussion was not limited to just writing the code. I also had to explain my approach and think through the follow-up questions.

2. System Design - LLD + HLD

This round involved designing a SaaS platform similar to Hugging Face. The discussion covered both the low-level design of the components and the overall system architecture.

Discussed topic:

• Applying OOP principles and suitable design patterns.

• Handling multiple users and organizations through a multi-tenant architecture.

• Integrating external APIs and managing dependencies.

• Designing the system to scale as usage grows.

• Keeping the code modular and easy to test.

The main focus was on how I would structure the system and the reasoning behind my design choices.

3. AI Platform Engineering + HLD

This round was about designing a scalable and highly available AI platform.

The discussion covered several important aspects of building and running AI systems in production:

• Model routing and choosing the right model for different requests.

• Reducing latency and managing token usage and costs.

• Rate limiting and caching to handle requests efficiently.

• Using queues and asynchronous processing for long-running tasks.

• Monitoring system performance, handling failures, and maintaining reliability.

• Optimizing infrastructure and model costs as usage increases.

The focus was on how to build an AI platform that can handle growing traffic without compromising too much on performance, reliability, or cost.

4. Staff-Level Technical Leadership

The final area focused on technical leadership and decision-making.

Topics:

• Driving architecture decisions across multiple teams.

• Handling technical disagreements and choosing between different approaches.

• Evaluating architectural trade-offs.

• Mentoring engineers and helping them grow technically.

• Setting engineering standards and influencing technical decisions across teams.

This part focused on the kind of decisions a Staff Engineer needs to make beyond their individual technical contributions.

Hope this helps anyone preparing for Staff Engineer or AI Platform roles.