In the search for Machine Learning Engineer (MLE) roles, it’s becoming evident that a significant portion of these positions — though certainly not all — appear to favor candidates with PhDs over those with master’s degrees. LinkedIn Premium insights often show that 15–40% of applicants for such roles hold a PhD. Within large organizations, it’s also common to see many leads and managers with doctoral degrees.

This raises a concern: Is there an unspoken glass ceiling in the field of machine learning for professionals without a PhD? And this isn’t just about research or applied scientist roles — it seems to apply to ML engineer and standard data scientist positions as well.

Is this trend real, and if so, what are the reasons behind it?

I’m currently working on a production ML project, so I can’t share specific details about the domain or data.

We have a deployed model where performance looks stable in offline evaluation, but in real usage we suspect gradual drift. The challenge is that reliable ground truth only becomes available weeks or months later, which makes continuous validation difficult.

I’m trying to understand practical approaches teams use in this situation:

• How do you monitor model health before labels arrive?
• What signals have you found most useful as early indicators of drift?
• How do you balance reacting early vs avoiding false alarms?

Looking for general patterns, tooling approaches, or lessons learned rather than domain-specific solutions.

In many ML systems, performance doesn’t collapse overnight. Instead, small inconsistencies creep in. A prediction here needs a manual override. A segment there starts behaving differently. Over time, these small exceptions add up and people stop treating the model as a reliable input for decisions.The hard part is explaining why this is happening, especially to stakeholders who only see aggregate metrics. For those who’ve been through this, what helped you surface the real issue early better monitoring, deeper segmentation, or a shift in how success was measured?

In development, machine learning models often feel predictable. Training data is clean, features are well understood, and validation metrics give a clear sense of confidence. But once the model is deployed, it starts interacting with real users, live systems, and data pipelines that were never designed for ML stability. Inputs arrive late or incomplete, distributions shift, and user behavior changes in ways the model has never seen before.

What makes this especially challenging is that these issues rarely show up as hard failures. The model keeps running, metrics look acceptable, and nothing triggers immediate alarms. Over time, though, performance drifts, trust erodes, and teams struggle to explain why outcomes no longer match expectations. Curious to hear from this community—what was the first real-world signal that told you your ML model was no longer operating under the assumptions it was trained on, and how did you respond?

Once an ML system moves from a controlled development environment to real-world traffic, the very first cracks tend to appear not in the model, but in the data pipelines that feed it. Offline, everything is consistent schemas are fixed, values are well-behaved, timestamps line up, and missing data is handled properly. The moment the model is deployed, it becomes completely dependent on a chain of upstream systems that were never optimized for ML stability.

Machine learning models are usually trained and validated in controlled environments where the data is clean, well-structured, and stable. Once deployed, the model becomes dependent on live data pipelines that were not designed with ML consistency in mind. Data can arrive with missing fields, schema changes, delayed timestamps, or unexpected values. At the same time, real users behave differently than historical users, causing gradual shifts in feature distributions. These changes don’t immediately break the system, but they slowly push the model outside the conditions it was trained for.