Reference checks remain one of the most underutilized signals in the hiring process for engineering and AI roles. While technical interviews assess a candidate’s skills in a controlled moment, references assess performance over time, which is where most bad hires reveal themselves. They provide context on how a candidate operates in real working environments, including consistency, collaboration, and how they handle challenges that do not surface in interviews. Here are 20 reference check questions you should ask when hiring technical candidates.
Key Takeaways
Structured reference check questions reduce risk in engineering and AI hiring by validating claims that resumes and interviews cannot verify, with a focus on technical context, code quality, collaboration patterns, and learning speed rather than general personality traits.
Reference checks are most useful when conducted with former managers and technical peers who directly reviewed the candidate’s work, using a consistent question set across finalists to keep evaluations comparable and defensible.
Structured reference feedback also supports onboarding by surfacing strengths, gaps, and management preferences early.
Why Do Reference Check Questions Matter?
Reference checks are uniquely valuable in software, data, and AI roles where a candidate’s professional history and real impact are difficult to judge from resumes alone. A candidate can describe systems on paper, but only references who worked alongside them can confirm what actually happened in practice.
Technical interviews often over index on algorithm puzzles and underweight long-term behaviors like maintainability, documentation, and collaboration. A candidate’s ability to solve a whiteboard problem tells you little about whether they write clean, tested code or respond well to feedback in code reviews. Thoughtful reference questions help validate core claims about systems built, stack used (for example Python, TypeScript, Kubernetes, PyTorch), and scale handled.
Fast-growing startups and AI companies are under pressure to move quickly, which increases the risk of skipping or rushing reference checks. This is a mistake. When there is uncertainty about a candidate’s technical skill, placing more weight on references from people who directly reviewed code, architecture decisions, or ML models provides more credible validation of technical depth claims. Marketplaces such as Fonzi can help by pre-vetting engineering talent and making it easier to reach credible references, but judgment about what references reveal should remain with the hiring manager.
Who to Call for Technical Reference Checks
Prioritize direct managers and tech leads who reviewed code, architecture, or ML models, rather than HR-only contacts. A reference from someone who never directly observed the candidate’s work provides limited insight. For senior engineers and staff-level candidates, at least one reference should be a former manager and one should be a senior peer or tech lead from a major project.
Ask the candidate to introduce references over email and to explicitly request former managers from the past three to five years, while respecting confidential searches involving their current employer. This warm introduction increases response rates and helps build initial trust before your call.
Open every reference call with a clear agenda, an expected duration of 15 to 20 minutes, and reassurance that feedback will be treated confidentially. This sets a professional tone and encourages more candid responses from the reference.
Choosing the Right Technical References and Focus Areas
The following table helps hiring managers choose appropriate references and tailor questions based on the working relationship each reference had with the candidate.
Reference Type | Relationship to Candidate | Best Focus Areas for Your Questions |
Former Engineering Manager | Direct supervisor who conducted performance reviews and assigned work | Overall job performance, growth trajectory, work ethic, feedback response, rehire likelihood |
Tech Lead or Architect | Technical leader who reviewed code, design docs, or architecture decisions | Code quality, system design, technical judgment, incident response, decision making |
Senior Peer Engineer | Colleague who collaborated on projects, participated in code reviews in GitHub or GitLab | Collaboration in agile sprints, code review culture, interpersonal skills, reliability |
Cross-Functional Partner (PM, Data Scientist) | Worked on shared projects across functions | Communication with non-technical stakeholders, handling disagreements over scope, model deployment practices for AI roles |
Reference Check Questions for Validating Technical Performance
These questions are designed to cross check technical depth, problem solving, and reliability in production environments. They go beyond the candidate’s resume to uncover how the candidate performed in real situations over extended periods.
The 20 questions below are grouped by theme. The interviewer should pick a consistent subset for all finalists for a given role to keep evaluations comparable. Each question should be followed by neutral probes like “Can you share a specific example where you saw this in practice?” to ground answers in real work. Names in examples are placeholders, and hiring teams should always use the candidate’s actual name during reference conversations.
These questions are tuned for software engineers, data engineers, and ML or AI engineers, but can be lightly adapted for SREs, platform engineers, and technical leaders.
Questions 1 to 5: Role, Scope, and Technical Environment
These questions confirm the candidate’s role and establish context for evaluating subsequent answers about performance and impact.
Can you describe the candidate’s title, team, and reporting line, as well as the projects and systems they owned between specific dates? This helps verify the candidate’s level and scope against what appears on their resume.
What was the primary tech stack the candidate used, such as React, Go, AWS, PostgreSQL, TensorFlow, or Vertex AI, and how deeply did they work in each area? This clarifies whether the candidate’s skills match the role requirements.
Can you describe the complexity and scale of systems the candidate worked on, including request volume, data sizes, or latency constraints, and what ownership they had within those systems?
How closely did you work with the candidate, and how often did you review their code, design docs, or experiment reports? This helps gauge the reliability of the reference’s perspective.
What was the candidate’s work style when it came to deadlines and delivering on commitments? This surfaces early signals about work ethic and reliability.
Questions 6 to 10: Code Quality, System Design, and Technical Decision Making
These questions uncover how the candidate writes and maintains production code and designs systems over time. They move beyond stated strengths to explore how they handled complexity.
How would you describe the candidate’s typical code quality in code reviews, including readability, testing habits, and use of documentation and comments?
Can you tell me about a significant technical design or architecture decision the candidate led or influenced, and what trade offs they identified at the time?
How did the candidate approach technical debt, refactoring, and long term maintainability on their team’s services or ML pipelines?
On a scale of 1 to 10 for this candidate’s level, such as mid level, senior, or staff, how would you rate their technical judgment? Can you share a recent example that supports that rating?
Can you describe a specific project where the candidate’s technical decisions had a measurable impact on system reliability, performance, or user experience?
Questions 11 to 15: Collaboration, Communication, and Feedback Culture
Even very senior engineers fail if they cannot collaborate effectively across engineering, product, and data functions. These questions reveal the candidate’s communication skills and how they work with coworkers.
How did the candidate participate in code reviews? Did they give specific, respectful feedback, and how did they handle review comments on their own pull requests?
Can you describe the candidate’s cross functional work with product managers, designers, or data scientists? How did they handle disagreements over scope, timelines, or technical constraints?
How did the candidate communicate trade offs to non technical stakeholders, such as during roadmap discussions or incident reviews?
Can you share an example of how the candidate responded to constructive criticism? Did they change their approach after receiving feedback?
How did the candidate handle feedback from direct reports or more junior team members if they were in a leadership position?
Questions 16 to 20: Learning Speed, Ownership, and Risk Signals
These questions surface professional growth potential and any reasons to proceed cautiously before extending a job offer.
How quickly did the candidate ramp up on a new stack or domain, such as moving from on premises systems to cloud infrastructure or from classical ML to large language models?
Can you describe a challenging production incident, on call escalation, or model failure the candidate helped resolve? What role did they play, and how did they handle stressful situations?
What was the most significant development area you observed in the candidate, and what support or environment did they need to perform at their best?
Would you hire or work with the candidate again in a similar engineering or AI role? What type of team or scope would be the best fit for their professional development?
Is there anything else about the candidate’s past performance that would help employers understand whether they are the right person for a senior technical role?
Adapting Reference Check Questions by Seniority and Role
Engineering managers should not use the same script for a new graduate backend engineer and a staff ML architect. The questions should match the candidate’s level and the expectations of the open position.
For junior engineers, emphasize hands-on coding, mentoring potential, and basic reliability. Ask references how the candidate handled unexpected challenges, whether they asked for help appropriately, and how quickly they learned new skills.
For senior and staff engineers, shift questions toward system ownership, cross team influence, incident leadership, and architectural decision making. Ask how the candidate influenced colleagues and whether they drove technical direction across multiple teams.
For different tracks such as backend, frontend, data engineering, machine learning, and applied AI, adapt questions with domain specific probes. For backend engineers, ask about API design and database performance. For ML engineers, ask about experimentation rigor and model deployment.
For engineering managers and heads of AI, questions should also explore hiring track record, team health, and collaboration with product and business stakeholders rather than hands on coding ability.
Adjusting Questions for AI and ML Engineering Roles
AI and ML work involves aspects such as experimentation, evaluation metrics, and model deployment risks that standard software engineering questions may not cover.
Add questions about how the candidate handled issues like model drift, data quality problems, and fairness or bias concerns in production ML systems. Ask references to describe the candidate’s rigor in experimentation, documentation of metrics, and ability to explain results to non ML stakeholders.
Include a question on how the candidate collaborated with data engineers, MLOps, and product teams to move from research notebooks to deployed services. This shows whether the candidate can bridge the gap between experimentation and production, which is critical for AI roles.
Running Structured, Compliant, and Efficient Reference Checks
Structure and consistency are essential for fairness, legal defensibility, and signal quality in hiring environments. Using the same reference check process for all finalists reduces bias and makes comparison easier.
Use a standard reference check template or scorecard that includes the 20 questions grouped by theme, with space for ratings and verbatim notes. This helps hiring managers track both positive and negative feedback in a systematic way.
Avoid questions that touch on protected characteristics such as age, marital status, health conditions, or nationality. Keep the focus strictly on job related behaviors and the candidate’s skills relevant to the role. This protects both the candidate and your organization.
For high volume technical hiring, teams can use simple automation, ATS workflows, or platforms to schedule and track reference outreach without outsourcing judgment. The interpretation of reference information should remain with the hiring manager.
Close every reference call by summarizing what you heard, checking for anything you missed, and thanking the reference for their time and examples. This ensures clarity and leaves a positive impression that may help with future outreach to the same previous employers.
Conclusion
Structured, role specific reference check questions make conducting reference checks a powerful signal in engineering and AI hiring rather than a perfunctory checkbox. The 20 questions outlined here help validate technical impact, collaboration style, and growth potential in ways employment interviews alone cannot match.
Standardize a reference question set for your next engineering or AI search and review outcomes after a few hiring cycles. Track which questions yield the most valuable information and refine your approach over time to make an informed hiring decision consistently.
FAQ
What reference check questions should I ask when hiring for engineering roles?
How do I evaluate a candidate’s technical ability through a reference check?
What questions help uncover how an engineer works on a team and handles code reviews?
What red flags should I listen for during a reference check for a technical hire?
How many references should I check for engineering candidates and who should I talk to?
