Hiring an AI or ML developer who can't communicate progress, blockers, and technical decisions will derail your project faster than a poorly tuned model. Strong communication skills separate senior engineers who unblock teams from individual contributors who disappear into black boxes. Here's how to identify and vet those communication capabilities before onboarding.
Why Communication Matters in AI/ML Teams
AI projects involve uncertainty. Unlike traditional software, ML workflows include experimentation phases where results are unpredictable—hyperparameter tuning fails, data quality issues surface mid-pipeline, and model drift appears in production. A developer who can articulate why a particular approach failed and what they're trying next is invaluable. Poor communicators leave stakeholders confused about timelines, budget overruns, and project viability.
Additionally, ML teams are inherently cross-functional. Data engineers, product managers, and business stakeholders need to understand model limitations, inference costs, and training requirements. Someone strong in communication can translate technical constraints into business impact.
Assess Written Communication During Screening
Request a brief technical write-up before the interview—something realistic like "explain a past ML project's architecture and why you chose that approach" (300-500 words). Don't ask for a polished essay; you want to see if they can structure thinking clearly.
Look for:
- Logical flow: Does the explanation move from problem → approach → results → lessons learned?
- Appropriate depth: Do they assume too much knowledge or over-simplify?
- Honesty about trade-offs: Do they acknowledge why they didn't use approach X instead of Y?
- Concrete examples: Are metrics, libraries, and data sizes mentioned, or is it vague?
Weak communicators often produce rambling paragraphs full of jargon without context. Strong ones explain decisions in terms of constraints (latency requirements, data volume, available compute).
Test Real-Time Explanation Skills
During technical interviews, assign a problem and ask them to think aloud as they solve it. This isn't about getting the answer right—it's about hearing their reasoning. Ask follow-up questions like "why did you choose scikit-learn over XGBoost here?" or "what's your concern with that approach?"
Pay attention to whether they:
- Ask clarifying questions upfront
- Break the problem into smaller components verbally
- Acknowledge uncertainty ("I'm not sure if that's the right metric here")
- Course-correct when you challenge an assumption
Someone who explains clearly will walk you through their thinking. Someone who struggles will give one-word answers or become defensive.
Evaluate Cross-Functional Collaboration Examples
Ask direct questions about past work with non-technical stakeholders:
- "Tell me about a time a business team didn't understand why a model couldn't meet their requirement. How did you handle it?"
- "Describe a situation where you had to justify why retraining the model would take 3 weeks instead of 3 days."
- "Have you documented model limitations or performance trade-offs for a non-technical audience?"
Look for evidence of patience, simplification without condescension, and willingness to iterate on explanations. Poor answers sound like "they just didn't understand ML" rather than "I worked with them to align on realistic expectations."
Check References for Communication Patterns
When contacting references (ideally previous managers or project leads, not just peers), ask specifically: "How did this person communicate blockers or setbacks? Did you always know the project status?" References will often reveal whether someone proactively updates stakeholders or goes silent until problems explode.
Practical Vetting Checklist
- Written sample explaining a past project (focus on structure, not length)
- Real-time problem-solving with verbalized thinking
- Specific examples of explaining technical constraints to non-technical people
- Reference feedback on communication consistency and transparency
- Comfort with admitting uncertainty or knowledge gaps
When to Prioritize Communication
If you're building an in-house ML team or managing a contractor, communication strength matters even more than raw technical skills. A competent engineer who keeps you informed beats a brilliant one who disappears for weeks. For smaller projects with tight timelines (3-6 months), clear communication prevents scope creep and misaligned deliverables.
Platforms like Mercoly let you compare and evaluate AI & Machine Learning Development providers side-by-side, including team communication practices and past project documentation—making it easier to spot communicators versus code-only developers.
Frequently Asked Questions
Q: How can I assess if an AI contractor will communicate throughout the project? Look for a willingness to commit to specific update cadences (weekly status calls, written summaries) in your contract, and ask references whether they volunteer information or require prompting.
Q: What's a red flag for poor communication in technical interviews? Defensiveness when questioned, vague answers ("I used machine learning to solve it"), or inability to explain why one approach beats another.
Q: Should I prioritize communication skills for junior ML engineers? Yes—junior engineers especially should be coachable, ask clarifying questions, and openly acknowledge what they don't know rather than bluffing through explanations.
Start your vetting process today by writing down what "clear communication" looks like for your specific project needs.