The interviewer defaulted to older stuff on my resume and I kind of just went along with it for a bit before realizing I had more relevant recent work I should be talking about.
Select 2-3 projects that showcase your ML engineering skills, emphasizing technical trade-offs and adaptability to ambiguity. For each, briefly describe the problem, your approach, the impact, and key learnings. Tailor your descriptions to LinkedIn's scale and data-driven culture.
Pro tip: Quantify impact with metrics (e.g., latency reduction, accuracy improvement) and explicitly discuss trade-offs you made, showing you understand engineering constraints. This demonstrates maturity and aligns with LinkedIn's focus on measurable results.
Choose 2-3 projects that highlight ML engineering, scalability, and handling ambiguity. Prioritize those with clear impact and technical depth.
For each project, briefly explain the problem, your role, and the constraints (e.g., data size, latency requirements). This frames your contributions.
Outline your methodology, including model choices, data processing, and infrastructure. Highlight trade-offs (e.g., accuracy vs. speed) and how you navigated ambiguity.
Quantify results (e.g., improved CTR by X%, reduced training time by Y%). Share key learnings and how they apply to future work.
Relate your experience to LinkedIn's scale, products, or values (e.g., member-first, data-driven). Show enthusiasm for applying your skills.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.