How do you prioritize and influence?

A strong answer should be structured, concrete, and leadership-oriented. For behavioral interviews, the best format is usually **STAR**: **Situation, Task, Action, Result**. ## 1) Prioritizing multiple projects ### What the interviewer wants to assess - Whether you can make decisions under ambiguity - Whether you understand business impact, not just technical difficulty - Whether you communicate trade-offs clearly - Whether you can align engineering work with product goals ### Strong answer structure **Situation:** Briefly describe a time when you had several competing initiatives. **Task:** Explain what made prioritization difficult: limited time, limited people, conflicting deadlines, or unclear requirements. **Action:** Show a clear framework. For example: - Estimate business value or customer impact - Assess urgency and deadline rigidity - Evaluate technical risk and downstream dependencies - Consider effort and opportunity cost - Align with leadership or stakeholders on trade-offs - Revisit priorities as new information arrives **Result:** Quantify outcomes when possible: - Launch timeline met - Revenue, engagement, latency, or model quality improved - Reduced incident risk - Better cross-team alignment ### Example talking points - "I ranked projects by customer impact, launch dependency, and execution risk." - "I separated must-do work from nice-to-have work." - "I communicated what would be delayed and why, rather than pretending everything could be done simultaneously." - "I created a milestone plan so stakeholders could see the consequences of each prioritization choice." ### Common mistake to avoid Do not say only, "I worked harder and did everything." Interviewers want judgment, not just effort. --- ## 2) Asking for more resources ### What the interviewer wants to assess - Influence without authority - Ability to advocate using data rather than emotion - Strategic thinking under constraints - Stakeholder management ### Strong answer structure **Situation:** Describe a case where demand exceeded team capacity. **Task:** Explain why extra resources were needed: critical launch, infrastructure bottleneck, model quality gap, compliance need, or reliability risk. **Action:** Show that you built a business case: - Clarified the gap between goals and current capacity - Quantified impact of under-resourcing - Proposed options, not just a complaint - Identified exactly what was needed: headcount, contractor support, labeling budget, compute budget, or help from partner teams - Offered trade-offs if resources could not be granted **Result:** Explain what happened: - Resources approved - Scope adjusted intelligently - Timeline renegotiated - Risk reduced through phased delivery ### Strong framing A mature answer sounds like this: - "I did not simply ask for more people. I showed the impact of the resource gap and proposed multiple paths forward." - "I framed the request in terms of product outcome, customer experience, and delivery risk." ### Good resource request components - Current workload vs. required workload - Cost of delay or failure - ROI of additional support - Clear ownership and execution plan - Backup plan if request is denied ### Common mistake to avoid Do not frame the answer as escalation or complaining. The best answers show partnership and options. --- ## 3) Most challenging project ### What the interviewer wants to assess - Depth of ownership - Technical and organizational complexity - Resilience and decision-making - Ability to learn and adapt ### Strong answer structure Choose a project with genuine complexity, such as: - Conflicting stakeholder goals - Tight timeline with incomplete data - Production ML system failure or degraded model performance - Large-scale migration or platform change - Ambiguous product requirements with major business consequences Then structure your answer: **Situation:** What was the project and why was it hard? **Task:** What were you personally responsible for? **Action:** Focus on decisions and trade-offs: - Broke down an ambiguous problem - Coordinated across engineering, product, data science, or infrastructure - Identified the highest-risk components early - Used experiments or metrics to guide decisions - Adjusted plan when assumptions failed **Result:** Share measurable outcomes and what you learned. ### What makes a strong MLE example For a machine learning engineering role, a strong story often includes: - Data quality or labeling issues - Offline metrics vs. online performance mismatch - Model deployment or serving constraints - Latency, scale, cost, or reliability trade-offs - Collaboration with product, infra, and research partners ### Strong closing End with reflection: - What you would do differently now - What principle you learned - How the experience improved your leadership or execution --- ## Overall interview strategy ### What good answers have in common - Specific examples, not generic philosophy - Clear ownership: what **you** did - Measurable outcomes - Evidence of collaboration and influence - Honest trade-offs and lessons learned ### Recommended answer length For a 30-minute behavioral round with several questions: - Spend about 1-2 minutes setting context - Spend 2-3 minutes on actions and decisions - Spend 30-60 seconds on results and lessons ### Final tip Prepare 3-5 reusable stories that can flex across questions: - Prioritization under pressure - Influencing for resources or alignment - Managing conflict across teams - Recovering from failure or ambiguity - Delivering a difficult project end-to-end If your stories include business impact, technical judgment, and collaboration, they will work especially well for an AI/ML engineering interview.