Plan and lead a large recommendation project
Company: LinkedIn
Role: Machine Learning Engineer
Category: Behavioral & Leadership
Difficulty: medium
Interview Round: Onsite
You are given a recommendation design problem, but the interviewer focuses on leadership and execution rather than detailed modeling.
Explain how you would:
1) Turn a high-level recommendation idea into an execution plan (milestones, dependencies, risks).
2) Estimate staffing: what roles you need (e.g., MLE, DS, backend, product, design, data engineering) and roughly how many people.
3) Propose a realistic timeline (phased rollout) and how you would coordinate cross-functionally.
4) Define success metrics and how you would run experiments.
5) Handle trade-offs, disagreements, and communication to stakeholders.
Overview: This Behavioral & Leadership question evaluates leadership, execution planning, cross-functional coordination, resourcing, and stakeholder communication skills in the context of machine learning recommendation product development.
Read the full LinkedIn Machine Learning Engineer interview experience this question came from
Solution
## 1) From idea to plan
- **Clarify goal + scope**: what user problem, where recommendations show up, what actions we optimize (click, start, completion, downstream retention).
- **Write a 1–2 page spec**:
- Problem statement, users, surfaces.
- Success metrics + guardrails.
- Constraints (latency, privacy, fairness, explainability).
- Assumptions and open questions.
- **Decompose into workstreams**:
1) Data/Logging readiness
2) Candidate generation
3) Ranking model
4) Serving + integration
5) Experimentation + analytics
6) Trust/safety/compliance
- **Milestones** (example):
- M0: metrics + instrumentation finalized
- M1: baseline heuristic recommender + offline eval
- M2: first ML ranker + shadow launch
- M3: A/B test + iteration
- M4: scale + monitoring + handoff
## 2) Staffing estimate (typical)
Depends on surface criticality and infra maturity, but a common “medium-sized” build:
- **Tech lead (you)**: 1
- **MLEs**: 2–3 (retrieval/ranking, training pipeline, evaluation)
- **Data engineer**: 1 (event pipelines, feature tables)
- **Backend/infra engineer**: 1–2 (online service, caching, latency)
- **Data scientist/analyst**: 1 (metrics, experiment analysis, guardrails)
- **PM**: 1 (requirements, stakeholder alignment)
- **Design/UX**: 0.5–1 (surface + explanations)
- Optional: **Privacy/legal**, **Trust & Safety**, **SRE** as part-time reviewers.
## 3) Timeline and phased rollout
A realistic phased approach (e.g., 12–16 weeks depending on infra):
- **Weeks 1–3**: requirements, logging gaps fixed, baseline established.
- **Weeks 4–7**: candidate generation + offline evaluation; first integration.
- **Weeks 8–11**: ranking model + calibration; shadow traffic; load testing.
- **Weeks 12–14**: A/B test, iterate, add re-ranking constraints (diversity/freshness).
- **Weeks 15–16**: ramp to 100%, monitoring, playbooks, post-launch review.
Cross-functional coordination:
- Weekly stakeholder sync; clear RACI; shared dashboard; written decision logs.
## 4) Success metrics and experimentation
- **Primary**: conversion aligned to product (e.g., starts/completions; qualified leads; retention).
- **Guardrails**: latency, bounce, hides/reports, content diversity, fairness segments.
- **Experiment plan**:
- Pre-register hypotheses.
- Define sample size/power.
- Run staged ramp (1% → 10% → 50% → 100%) with stop conditions.
## 5) Trade-offs and conflict handling
- Make trade-offs explicit: “optimize CTR vs long-term value,” “freshness vs relevance,” “complexity vs reliability.”
- Use **principles + data**:
- Start with simplest baseline that ships.
- Use offline results to narrow options; use A/B tests to decide.
- Communication:
- Frequent written updates.
- Escalate early when timelines/risks change.
- Document decisions and why alternatives were rejected.