Describe a recent project and your biggest challenge
Company: Zillow
Role: Machine Learning Engineer
Category: Behavioral & Leadership
Difficulty: medium
Interview Round: Technical Screen
## Behavioral questions
You have **5–10 minutes** to answer, using a structured approach (e.g., STAR).
1. **Self-introduction:** Give a concise overview of your background and what you’ve been working on recently.
2. **Deep dive on a recent project:**
- Pick one project you worked on recently (ideally end-to-end impact).
- Explain the goal, your role, scope, stakeholders, and what you personally delivered.
- Be prepared for clarification questions (requirements, constraints, trade-offs, and results/metrics).
3. **Biggest difficulty:** Describe the **most difficult problem** you encountered in that project.
- What made it hard (technical ambiguity, data issues, scaling, cross-team alignment, timeline, etc.)?
- What actions did you take?
- What was the outcome and what did you learn?
**Goal:** Demonstrate ownership, communication, and learning mindset—not just technical details.
Quick Answer: This question evaluates ownership, communication, problem-solving, stakeholder management, and the ability to articulate technical impact in a Machine Learning Engineer context, testing behavioral and leadership competencies.
Solution
### What a strong answer looks like
#### 1) Self-introduction (60–90 seconds)
Use a tight structure:
- **Present:** role + domain focus (e.g., “I build LLM/ML systems for X”).
- **Past:** 1–2 relevant experiences.
- **Strengths:** 2–3 skills tied to the job (modeling, productionization, experimentation, cross-functional work).
- **Hook:** what you’re looking to do next.
Keep it specific and measurable where possible.
#### 2) Project deep dive: use an “executive summary + drill-down” format
A clear template:
- **Problem:** What user/business pain were you solving?
- **Success metrics:** What did “good” mean? (latency, cost, accuracy, CTR, win-rate, human eval scores, etc.)
- **Constraints:** data availability, privacy, compute budget, timeline, reliability, integration requirements.
- **Your role:** what you owned end-to-end vs. contributed.
- **Approach:** key decisions and trade-offs (baseline → iterations).
- **Results:** quantified impact + confidence (A/B test, offline eval, human eval, error analysis).
- **Follow-ups:** what you would do next, known limitations.
**Common clarification questions to prepare for**
- What was the baseline and how did you compare?
- What were the failure modes and how did you diagnose them?
- What trade-off did you make between quality vs. latency/cost?
- How did you ensure reproducibility and correctness?
- What did you deprecate or decide not to do (and why)?
#### 3) Biggest difficulty: answer with STAR, emphasize decision-making
**STAR outline (high-signal):**
- **S (Situation):** one sentence context.
- **T (Task):** what you were responsible for.
- **A (Action):** 3–5 concrete actions (prioritization, experiments, alignment, mitigation plans).
- **R (Result):** measurable outcome + what changed.
**Add a “learning + prevention” close:**
- What you learned.
- What process/tech change you introduced to prevent recurrence (e.g., better data validation, eval harness, rollout guardrails, documentation).
#### Pitfalls to avoid
- Too much product/company background; not enough *your* contribution.
- Claiming impact without metrics or evaluation method.
- Describing the difficulty as purely external (“other team blocked me”) without showing your actions.
- No reflection: interviewers look for growth and judgment.