GenerativeModels.ai
Engineering Interview Process Overview

Engineering Interviews

🧠 Philosophy

Interviews are designed to assess whether a candidate will be a strong contributor to our company’s mission.

This is a difficult task—and making a reliable judgment based on a 30–60 minute conversation is inherently prone to error.

However, structured interviews are still the most scalable way to filter out unsuitable candidates efficiently.

🔍 What We Aim to Identify

During each interview, we focus on identifying core traits that predict success at GenerativeModels.ai:

  1. Motivation:

    Is the candidate genuinely driven by solving hard problems? Will they stay motivated over time?

  2. Problem-Solving Ability:

    Can the candidate apply strong logical thinking and reasoning to tackle complex challenges?

  3. Independent Execution:

    Can they design and implement solutions independently without excessive supervision?

  4. Resilience and Grit:

    Are they willing to do the necessary but less glamorous parts of the work to bring projects to completion?

  5. Goal and Outcome Orientation:

    Are they focused on delivering results rather than just completing tasks?

  6. Transparency and Trustworthiness:

    Are they honest, self-aware, and willing to admit when they don’t know something?

  7. Reliability:

    Can they be counted on to follow through on commitments consistently?

🛠️ Interview Funnel

Because evaluating all these traits in a single session is difficult, we use a multi-step interview process:

flowchart TD
    A[Initial Interview: Background + Simple Coding Task] --> B{Pass?}
    B -- No --> X[Send Respectful and Supportive Rejection Email]
    B -- Yes --> C[On-Site Interview: Repo Setup Exercise]
    C --> D[On-Site Interview: Past Project Deep Dive]
    D --> E{Final Decision}
    E -- Accept --> Y[Offer Extended]
    E -- Reject --> Z[Send Respectful and Supportive Rejection Email]

Step 1: Initial Interview (Screening)

We start with a short conversation covering the candidate’s background, followed by a simple real-world coding exercise.

The purpose here is not to test how many algorithms they have memorized or to assess their IQ through puzzles.

Instead, we want to answer a basic but critical question:

Can the candidate solve a straightforward real-world problem using code?

Examples of initial problems include:

  • FizzBuzz
  • Peak finding
  • Simple string or array manipulation tasks (on a LeetCode easy to medium level)

During this phase:

  • We observe problem-solving process, not just correctness.
  • We look for clarity of thought, logical progression, and basic coding competence.

Important:

More than 50% of candidates typically fail at this stage, often because they lack basic coding ability.

If the candidate passes, we ask follow-up questions based on the role’s needs:

  • Algorithmic optimization
  • Scalability considerations
  • Code clarity and naming (e.g., “What would you rename this function?”)

Step 2: On-Site Interview (Technical Deep Dive)

Candidates who pass the screening are invited to an on-site (or extended remote) interview focused on hands-on skills.

Key parts of this session:

  • Repo Setup Exercise:

    The candidate is asked to clone one of our repositories and run it locally on their own machine.

    We specifically observe:

    • Whether they read and follow the README carefully
    • How they troubleshoot setup and environment issues independently
    • Their familiarity with package managers, virtual environments, and basic DevOps tools
    • Their local development environment setup (e.g., what tools, shells, IDEs they use)

    This provides strong signal about how they will operate in a real-world, agent-driven, engineering environment.

  • Past Project Deep Dive:

    The candidate is asked to walk us through one of their past projects in detail.

    Signals we look for:

    • Transparency: Are they honest about what they know and don’t know?
    • Depth: Can they explain why they made certain technical decisions?
    • Breadth: Have they been exposed to multiple techniques, architectures, or tools?
    • Technical ownership: Did they truly drive parts of the project?

✏️ Closing Thoughts

Our interview process is not about “catching” candidates—it’s about finding builders who align with our mission, values, and bar for technical excellence.

We’re looking for people who:

  • Can reason through complexity
  • Can build and debug real-world systems
  • Are trustworthy, resilient, and goal-oriented

Every step of the process is designed to surface these traits as clearly as possible.

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