Short answer: Not exactly a glass ceiling, but there is definitely a perception bias—especially in research-heavy or top-tier tech companies.

Why PhDs are preferred? 

1. Signal of Research Rigor
   A PhD often signals that a candidate has been trained to think deeply, tackle open-ended problems, and publish novel ideas—skills valued in teams building foundational models or custom ML architectures.

2. Hiring Shortcut in Big Companies
   In large orgs with hundreds of applicants, recruiters may filter by academic credentials as a proxy for ability—especially for research-oriented or leadership roles.

3. Historical Bias
   ML and AI originated in academia, so many early leaders came from PhD programs. That legacy bias still shapes hiring in some orgs.

Why PhDs is not necessary?

1. Industry demands practical skills
   For MLE roles focused on deployment, scaling, monitoring, and optimization, engineering skills, systems thinking, and hands-on ML experience matter more than academic research.

2. Startups & applied teams value results
   In fast-paced or product-driven environments, your ability to ship, iterate, and impact the business is what really counts.

3. Strong portfolio beats credentials
   Open-source contributions, Kaggle achievements, published blogs, or real-world ML deployments often carry more weight than a degree.

What to do if you don’t have a PhD

• Build a standout project portfolio (with measurable impact)

• Contribute to OSS or research collaborations

• Lean into roles that value applied ML (MLOps, LLMOps, personalization, etc.)

• Network your way in—referrals often override HR filters

• Consider research-lite roles like Applied Scientist or MLE on product teams

What I think there’s no hard ceiling—but certain doors may take more effort to open without a PhD. That said, the industry is shifting toward skills-first hiring. And with LLMs, MLOps, and real-world deployment taking center stage, engineering excellence is more valuable than ever.