Completed a lengthy interview process (~1 month total, from 4/14/26 to 5/12/26, including nearly 3 weeks between final interviews and the final compensation update) for a Senior Machine Learning Engineer / MLOps-focused role centered around production ML infrastructure, platform ownership, observability, cloud-native architecture, and cross-functional technical leadership. Before entering the process on 4/14/2026, I explicitly communicated compensation expectations (~$200k+ base, or slightly lower with a strong sign-on component). The recruiter shared a target range of roughly $165k–$190k, stated the role had flexibility, and indicated there was likely room to bridge the gap. The role was also initially described as being scoped closer to staff-level responsibilities before later being adjusted downward internally. After completing 5 interview rounds between 4/15/26 and 4/24/26, I was verbally told on both 4/29/26 and 5/8/26 that the team was likely landing around $175k–$180k base pending final approval. However, after additional delays and follow-up, I was informed on 5/12/26 that the company had decided to relevel the role significantly more junior due to broader hiring changes and could now only support compensation around $130k. The disappointing part was not simply compensation changing — hiring priorities and budgets evolve. The issue was the amount of candidate time invested after compensation expectations, role scope, and seniority discussions had already been clearly established upfront. The recruiter was professional throughout, and I do not believe this was an individual recruiter issue. However, future candidates should strongly verify that leveling, budget approval, and role scope are finalized internally before committing to an extended interview process.