I went through Meta's full interview loop for an E5/E6 Software Engineer role. The process included the standard four-round onsite: two coding rounds, a system design round, a behavioral round, and an AI coding round that Meta has added to their loop. I prepared extensively for each stage — grinding LeetCode-style problems for the coding rounds, building a library of system design references, and writing out STAR-format behavioral answers calibrated to Meta's E5/E6 expectations, drawing on my Oracle Cloud Infrastructure work across the Multicloud Observability team (control plane unification, data plane migration to Oracle Managed Kubernetes, and the Oracle Database at AWS buildout). Ultimately, I received a rejection with a one-year cooldown before I can reapply.

Looking back honestly, a few weaknesses stood out:

• Coding execution under time pressure. While I could solve the problems, I wasn't always optimal on the first pass. I spent time re-deriving approaches instead of pattern-matching quickly, which cost me on the second problem in at least one round.
• System design depth vs. breadth tradeoff. My background is deep in cloud infrastructure and observability, so when the design prompt pulled toward consumer-scale product systems (feed ranking, social graph type problems), I leaned on general principles rather than Meta-specific intuition. I covered the fundamentals but didn't always drive the conversation into the nuanced tradeoffs interviewers wanted to hear.
• Behavioral calibration to Meta scale. My STAR stories were strong on technical substance, but a few of my impact framings were sized for Oracle's context rather than translated into the scale and cross-org influence language Meta's bar expects at E5/E6.
• AI coding round unfamiliarity. This was a newer format for me and I hadn't practiced it as deliberately as the traditional rounds, so my workflow with the AI tooling wasn't as fluid as it could have been.

The cooldown gives me a clear runway to address each of these before reapplying.