10 Essential Questions to Ask When Interviewing Software Engineers

1. Selecting Questions for the Software Engineering Role

Select software engineering interview questions that match the role’s responsibilities, seniority and technical stack. Look for evidence of how candidates reason through problems and explain their decisions.

If artificial intelligence assists with initial screening, interviewers still need targeted questions during interviews in order to assess engineering reasoning. Role requirements guide the application of structured rubrics and specific questions.

Jump to: the ten interview questions · scoring guidance · the interview checklist.

2. Core Traits to Look for in Software Engineering Candidates

When interviewing software engineering candidates, examine qualities that incorporate technological expertise with practical judgment and teamwork:

Domain Intuition: A keen ability to comprehend industry-specific matters quickly and apply that understanding to actual issues.
Problem-Solving: Outstanding analytical talents to break down complex engineering subjects and develop well-reasoned solutions.
Collaboration: Strong working abilities with cross-functional teams help with integrating changes into product development cycles.
Data Fluency: Individuals who have the ability to read data and make use of data-informed insights to drive engineering decisions, informing development processes.
Ownership: A feeling of ownership of features and systems should prompt applicants to follow through on problems and verify repairs, supporting product stability.
Empathy: Candidates who can comprehend the needs of the user and team dynamics to build more human and user-centric solutions.

3. Personal and Career Background

Interviewing software engineering candidates can reveal a combination of formal education and diverse industry experiences. These are possible backgrounds, not universal prerequisites; use interview evidence to assess the skills needed for the advertised role:

Bachelor’s in Computer Science or related field, perhaps followed by a Master’s in Software Engineering or specialized degrees like Data Science.
Experience in tech, finance, or healthcare industries, where candidates have applied engineering principles to large-scale projects.
Possible career paths include Junior Developer, DevOps Engineer, QA Analyst, or Systems Analyst, leading up to mid-level or senior engineering roles.
Some of them have contributed to open-source projects or kept individual coding portfolios to highlight employed skill sets.

4. Technical Skills and Experience

Interviewing Software Engineering candidates requires verifying the technical proficiencies needed for the role:

Proficiency in Relevant Programming Languages: Fluency in languages like Python, Java, and JavaScript should match the role’s needs when working with varied tech stacks; multiple languages are not always required.
Experience with Cloud Infrastructure (AWS, Azure, GCP): Familiarity with cloud services enables candidates to deploy scalable applications efficiently when the role involves cloud infrastructure.
Understanding of DevOps and CI/CD Pipelines: Knowledge of automated testing, containerization (Docker, Kubernetes), and continuous integration helps candidates collaborate effectively within development workflows.
Familiarity with Agile and Scrum Methodologies: Experience in iterative development helps candidates adapt to changing requirements and feedback.
System Design Expertise: The ability to architect scalable, distributed systems can be relevant for building reliable products at scale; match design depth to the role.
Data Structures and Algorithms Proficiency: Mastery of core CS fundamentals allows candidates to optimize solutions and collaborate with colleagues on algorithmic improvements.
Experience with AI/ML Frameworks (TensorFlow, PyTorch): Candidates who understand machine learning pipelines can work on model integration tasks when these are part of the role.

5. Soft Skills

Interviewing Software Engineering candidates should assess essential soft skills through job-relevant examples of effective collaboration:

Communication: Clear articulation of technical concepts and open dialogues with colleagues are vital for coordinated development efforts.
Team Practices: Alignment with company values and the ability to thrive within team dynamics should be assessed through examples of agreed job-relevant behaviors, not personal similarity.
Conflict Resolution: Skill in navigating disagreements helps candidates use evidence and human insights to resolve technical debates.
Self-Awareness: Knowing one's own strengths and weaknesses enables candidates to know when to seek input or additional expertise.
Adaptability: The capacity to adjust to new workflows and rapidly changing requirements is useful when the role or tools evolve.
Collaboration: Effective collaboration enables candidates to collaborate with human co-workers in a way that development processes integrate with the team’s work.

6. The Best Interview Questions to Ask and Why

Interviewing Software Engineering candidates effectively involves questions that uncover technical prowess, problem-solving approaches, and job-relevant teamwork. Choose core questions and illustrative probes in advance and apply them consistently:

“Can you describe your design contribution to a complex system and the tradeoffs you considered?” This question reveals the candidate’s system architecture skills through their own contribution. Discuss AI tools only if relevant to the role.
“How do you approach debugging a production issue involving multiple services?” This uncovers the candidate’s troubleshooting methodology and collaboration skills under pressure. Illustrative probe: “What evidence would you check first, and how would you verify the fix?”
“Tell me about a time you disagreed with a team decision and how you handled it.” This probes conflict resolution and communication abilities.
“What’s your experience with automated testing frameworks, and how do you ensure test coverage?” This evaluates technical depth in DevOps and CI/CD environments. Illustrative probe: “Which tests would you prioritize, and why?”
“How would you optimize performance for a high-traffic web application?” This question examines system design expertise and algorithmic thinking.
“Describe a time you used data to make a key technical decision.” This reveals data fluency and the candidate’s ability to interpret the evidence behind a decision.
“How do you stay current with emerging technologies and integrate them into your workflow?” This tests adaptability and continuous learning mindset.
“What’s your process for collaborating with product managers when requirements shift mid-sprint?” This highlights collaboration skills and agility in dynamic project environments.
“Can you walk me through a challenging code review you conducted and how you provided feedback?” This assesses ownership, attention to detail, and mentorship abilities.
“How would you choose an architecture for a new feature, including whether microservices are appropriate, considering scalability and maintainability?” This examines system design capabilities and forward-thinking. Illustrative probe: “What tradeoffs would justify separate services rather than a simpler design?”

7. Good vs. Bad Interview Questions

Good interview questions are typically open-ended, behaviour-based, and scenario-focused, enabling recruiters to gain deep insights into a candidate’s thought process, adaptability, and collaboration skills. For instance, “Describe a system you helped build and a design decision you made” encourages candidates to explain technical decisions and teamwork.

In contrast, bad questions are often leading, vague, or require only yes/no answers, which reveal little about the candidate’s depth of understanding or ability to handle real-world challenges. For example, “Do you know how to use AWS?” can serve as an initial qualification check, but does not establish depth. Illustrative follow-up: “Describe an AWS deployment you worked on, the decisions you made, and the tradeoffs you considered.” Use it only if AWS is relevant to the role.

8. Scoring Candidates Properly

A structured rubric can guide evaluation when interviewing software engineering candidates. OPM’s structured-interview guidance describes assessing job-related competencies through past experience or hypothetical situations: ask all candidates the same predetermined core questions in the same order and evaluate responses using the same rating scale and standards for acceptable answers. By assigning clear weights to each criterion, hiring teams can focus on the most critical skills and attributes for the role. Record supporting evidence and apply pre-agreed probes consistently; ratings inform human judgment, not an automatic hiring decision.

Illustrative debugging evidence scale:

1 — Limited diagnostic evidence: Suggests fixes without explaining how to investigate the cause.
2 — Evidence-based investigation: Uses logs, metrics or traces to test hypotheses.
3 — Investigation, mitigation and verification: Explains impact and mitigation, then checks whether the fix worked.

9. Red/Green Flags to Watch Out For

Being able to point out red and green flags can help focus follow-up when interviewing candidates. Treat them as prompts to investigate evidence, not predictions; avoid judging nervousness or polished delivery.

Red Flags:

The inability to explain technical findings after a pre-agreed probe warrants further investigation, not an automatic rejection.
Examples of refusing to collaborate or share credit warrant a probe about the candidate’s actions and context.
Overreliance on templated or AI-generated code without comprehending underlying logic warrants a probe about reasoning and verification.

Green Flags:

Proactively contributing ideas beyond assigned tasks can provide evidence of ownership and strategic thinking.
Evidence-backed explanations of past projects can provide evidence of communication and understanding of technical details.
Demonstrated experience improving processes (e.g., automating tests, optimizing pipelines) can provide evidence of adaptability and a continuous improvement mindset.

10. Common Interviewer Mistakes

When it comes to interviews for software engineers, unplanned questions and inconsistent criteria make evaluation harder. Mistakes include an emphasis on technical questions without assessing collaboration skills, the use of leading questions, and the neglect of calibrating interviewers. Mistakes like these compromise practical recruitment by obscuring evidence of relevant skills.

11. Tips for the Software Engineering Interview Process

Interviewing Software Engineering candidates requires a structured, candidate-centric approach to support consistent assessment and a positive candidate experience:

Define a Success Profile: Outline the required skills, traits, and experience for the role clearly in order to ensure alignment across stakeholders.
Use Structured Scorecards: When implementing rubrics in interviews and evaluations, record job-relevant evidence against pre-agreed rating standards. Optional tools can help organize notes, but interviewers should check the evidence.
Calibrate Your Interviewers: Ensure a consistent understanding of criteria and rating standards by comparing sample answers before interviews.
Limit Rounds to Key Stakeholders: Keep the interview process efficient by only involving integral parties, which tends to maintain engagement.
Allow Time for Candidate Questions: Being able to provide the space necessary for candidates to ask their questions can easily showcase their priorities and communication style.
Provide Prompt Feedback: Timely updates demonstrate respect and keep top candidates engaged.

12. How to Run Remote & Async Interviews That Actually Work

In remote and async contexts, structure and clarity are paramount when interviewing software engineering candidates to ensure a smooth, fair experience:

Select Appropriate Tools: Utilize platforms (e.g., Zoom, CodeSignal, HackerRank) appropriate to the interview’s coding or video needs; check access beforehand.
Design Realistic Assessments: Form coding exercises or take-home projects that mimic on-the-job challenges, with defined scope, time limits and allowed-tool rules.
Set Clear Expectations: Communicate timelines and evaluation standards upfront so candidates understand how to prepare and what to expect.
Standardize Evaluation: Use the same rubric for both synchronous and async interviews to maintain consistency.
Ensure Timely Communication: Deliver updates and feedback promptly, maintain engagement, and respect candidates' time.

13. Quick Interview Checklist

Interviewing Software Engineering candidates benefits from a quick-reference guide to keep processes streamlined and consistent:

Review Resumes with Optional AI Screening Tools: Leverage Litespace or similar AI as an optional aid to review resumes against the same job-relevant criteria, not to decide whom to hire.
Pre-Screen through a Short Phone or Async Call: Assess communication and basic technical knowledge before investing in deeper interviews.
Prepare a Structured Scorecard: Align interviewers on criteria and rating standards (e.g., Technical Proficiency, Problem-Solving). Record evidence for each rating.
Conduct Behavioral Interview with Pre-Agreed Questions: Use open-ended prompts to evaluate problem-solving and job-relevant teamwork.
Perform Technical Whiteboard or Coding Challenge: Simulate on-the-job tasks, focusing on algorithms and system design when relevant to the role.
Assess Soft Skills: Observe collaboration, adaptability, and communication throughout interviews.
Hold a System Design Interview When Relevant: Evaluate architecture thinking, scalability, and AI integration approaches; match design depth to the role and discuss AI integration only if needed.
Gather Stakeholder Feedback: Debrief with hiring manager and team to compare documented evidence against role criteria.
Check References with Focus on Past Work: Inquire about candidate’s experience in relevant work; include AI tools only if the role requires them.
Make Data-Driven Decision: Use scorecards to review evidence and unresolved questions; make a human decision against role criteria, not totals alone.
Extend Offer with Clear Timelines: Provide prompt, transparent feedback to maintain candidate engagement.
Plan Onboarding with Role-Relevant Training: Prepare resources to help new hires acclimate to the role’s workflows; include AI training only when needed.

14. Using Litespace to Improve Your Recruiting Process

Litespace’s AI Recruiting Assistant can help recruiters with initial screening. By utilizing AI-driven resume screening, recruiters can review resumes against their screening criteria, with rankings and explanations. Litespace’s product descriptions also describe AI pre-screening interviews as brief, role-specific Q&A based on screening criteria and the candidate’s resume. This can free up recruiters’ time for deeper dives into suitable candidates. Hiring teams remain responsible for assessing technical evidence and stay in control of every hiring decision.

Try Litespace today to enhance your recruiting process: https://www.litespace.io/

15. Final Thoughts

Structured interviews, clear evaluation criteria, and thoughtful best practices, encompassing both in-person and remote/async formats, can guide software engineering hiring. By defining rubrics, asking targeted questions, and maintaining consistent processes, recruiters can document job-relevant evidence and compare answers against agreed standards. Use that evidence to assess candidates, balancing technical expertise with soft skills, while keeping hiring decisions with the team.