10 Essential Questions to Ask When Interviewing Software Engineers (2025)

1. Why Software Engineering Interviews Are Getting Harder to Run

In 2025, the volume of applications for software engineers has increased, increasingly, AI-candidate batches are applying, making it more difficult to spot the best talent and necessitating more deliberate, systematic interviews. 

Despite the applications of artificial intelligence that can accomplish initial screening, interviewers need to invest additional time in more targeted screening during interviews in order to locate applicants who can improve AI technology. The pervasiveness of the use of AI-optimized resumes and the demand to satisfy increasingly changing technical demands justify the application of structured rubrics and specific questions. 

2. Core Traits to Look for in Software Engineering Candidates

In 2025, many things interviewers look out for when interviewing software engineering candidates are primarily examining qualities that incorporate technological expertise with the ability to adapt to AI-powered systems:

• Domain Intuition: A keen ability to comprehend industry-specific matters quickly and  use AI tools effectively to address actual issues.
• Problem-Solving: Outstanding analytical talents to break down complex engineering subjects and co-work alongside AI helpers in developing optimal solutions.
• Collaboration: Simultaneous working abilities with cross-functional teams and AI robots become critical to integrating automated operations into product development cycles.
• Data Fluency: Individuals who have the ability  to read data and make use of AI-powered insights to drive engineering decisions, making better and more efficient development processes.
• Ownership: A feeling of ownership of features and systems guarantees that applicants actually repair problems revealed by AI tools, making the product more stable.‍
• Empathy: Users who can comprehend the needs of the user and team dynamics to build more human and user-centric solutions in tandem with AI-based feedback.

3. Personal and Career Background

Interviewing software engineering candidates often reveals a combination of formal education and diverse industry experiences:

• Bachelor’s in Computer Science or related field, with many holding 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.
• Typical career paths include Junior Developer, DevOps Engineer, QA Analyst, or Systems Analyst, leading up to mid-level or senior engineering roles. 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 in 2025 requires verifying both foundational and emerging technical proficiencies:

• Proficiency in Multiple Programming Languages: Fluency in languages like Python, Java, and JavaScript is essential when working with varied tech stacks and AI integration frameworks.
• Experience with Cloud Infrastructure (AWS, Azure, GCP): Familiarity with cloud services enables candidates to deploy scalable AI-driven applications efficiently.
• Understanding of DevOps and CI/CD Pipelines: Knowledge of automated testing, containerization (Docker, Kubernetes), and continuous integration helps candidates collaborate effectively within AI-powered development workflows.
• Familiarity with Agile and Scrum Methodologies: Experience in iterative development ensures candidates can adapt to changing requirements and integrate AI-driven feedback loops seamlessly.
• System Design Expertise: The ability to architect scalable, distributed systems is crucial for building reliable AI-supported products at scale.
• Data Structures and Algorithms Proficiency: Mastery of core CS fundamentals allows candidates to optimize solutions and collaborate with AI tools that recommend algorithmic improvements.‍
• Experience with AI/ML Frameworks (TensorFlow, PyTorch): Candidates who understand machine learning pipelines can work more effectively with AI-driven development tools and collaborate on model integration tasks.

5. Soft Skills

Interviewing Software Engineering candidates must assess essential soft skills to ensure cultural fit and effective collaboration in AI-augmented environments:

• Communication: Clear articulation of technical concepts and open dialogues with AI-driven systems are vital for coordinated development efforts.
• Cultural Fit: Alignment with company values and the ability to thrive within team dynamics is leveraged by AI tools that measure engagement and collaboration.
• Conflict Resolution: Skill in navigating disagreements ensures that AI-suggested solutions are balanced with human insights to resolve technical debates.
• Self-Awareness: Knowing one's own strengths and weaknesses enables candidates to know when to use AI tools or draw on human experience.
• Adaptability: The capacity to adjust to new AI-driven workflows and rapidly changing requirements is critical in 2025’s dynamic tech landscape.
• Collaboration: Effective collaboration enables candidates to collaborate with human co-workers and AI systems in a way that automated processes integrate seamlessly.

6. The Best Interview Questions to Ask and Why

Interviewing Software Engineering candidates effectively in 2025 involves questions that uncover technical prowess, problem-solving approaches, and cultural alignment:

1. “Can you describe a complex system you designed from scratch and how you incorporated AI tools?” This question reveals the candidate’s system architecture skills and their ability to leverage AI in the design process.
2. “How do you approach debugging a production issue involving multiple services?” This uncovers the candidate’s troubleshooting methodology and collaboration skills under pressure.
3. “Tell me about a time you disagreed with a team decision and how you handled it.” This probes conflict resolution and communication abilities, crucial when AI tools suggest divergent strategies.
4. “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, where AI can generate test cases.
5. “How would you optimize performance for a high-traffic web application?” This question examines system design expertise and algorithmic thinking, especially relevant when AI suggests optimizations.
6. “Describe a time you used data to make a key technical decision.” This reveals data fluency and the candidate’s ability to interpret analytics provided by AI platforms.
7. “How do you stay current with emerging technologies and integrate them into your workflow?” This tests adaptability and continuous learning mindset.
8. “What’s your process for collaborating with product managers when requirements shift mid-sprint?” This highlights collaboration skills and agility in dynamic project environments.
9. “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, which remain vital despite AI-driven code suggestions.
10. “How would you design a microservices architecture for a new feature, considering scalability and maintainability?” This examines system design capabilities and forward-thinking, crucial when AI tools can propose architecture patterns.

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 when AI is part of the workflow. For instance, “Describe a system you built that leveraged AI-generated insights” promotes candidates to communicate both technical and collaborative patterns with AI tools. 

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?” leads to very little insight and does not demonstrate problem-solving or adaptability in candidates.

8. Scoring Candidates Properly

A structured rubric is essential when interviewing software engineering candidates because it enhances objectivity, reduces bias, and ensures consistent evaluation across multiple interviewers. By assigning clear weights to each criterion, hiring teams can focus on the most critical skills and attributes for success in 2025’s AI-augmented engineering roles.

9. Red/Green Flags to Watch Out For

Being able to point out red and green flags is essential when interviewing candidates because it helps differentiate between those likely to struggle and those heading toward success in AI-driven environments.

Red Flags:

• The inability to define technical findings indicates a lack of depth and adaptability when AI tools offer alternative answers.
• Hesitancy to collaborate or share credit indicates probable conflict when working alongside AI-driven teams.
• Overreliance on templated or AI-generated code without comprehending underlying logic implies shallow skills.

Green Flags:

• Proactively contributing ideas beyond assigned tasks indicates ownership and strategic thinking in AI-augmented workflows.
• Clear, concise explanations of past projects show strong communication and mastery of technical details.
• Demonstrated experience improving processes (e.g., automating tests, optimizing pipelines) reveals adaptability and a continuous improvement mindset.

10. Common Interviewer Mistakes

When it comes to interviews for software engineers, the most typical mistakes are the nonessential use of undeveloped techniques resulting in an inconsistent evaluation. Things of that nature would include an emphasis on technical questions without the mention of any soft skills, (which misrepresents team dynamics), the constant use of leading questions that result in biased answers, and the neglect of calibrating interviewers, resulting in scorer unreliability. Mistakes like these compromise practical recruitment by disregarding genuine potential.

11. Tips for the Software Engineering Interview Process

Interviewing Software Engineering candidates requires a structured, candidate-centric approach to ensure fair assessment and 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, it's set to reduce bias and increase consistency, especially when multiple interviews involve AI-generated results.
• Calibrate Your Interviewers: Ensure a consistent understanding of criteria so both parties are in alignment with one another.
• 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 trustworthy platforms (e.g., Zoom, CodeSignal, HackerRank) that support collaborative coding and video conferencing.
• Design Realistic Assessments: Form coding exercises or take-home projects that mimic on-the-job challenges, permitting candidates to showcase problem-solving without constraints.
• 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, especially when AI tools assist in initial screenings.‍
• Ensure Timely Communication: Deliver updates and feedback promptly, maintain engagement, and respect candidates' time, as the async processes tend to feel very disingenuous and disconnected.

13. Quick Interview Checklist

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

1. Review Resumes with AI Screening Tools: Leverage Litespace or similar AI to flag top candidates and ensure initial consistency.
2. Pre-Screen through a Short Phone or Async Call: Assess communication and basic technical knowledge before investing in deeper interviews.
3. Prepare a Structured Scorecard: Align interviewers on criteria and weights (e.g., Technical Proficiency, Problem-Solving).
4. Conduct Behavioral Interview with AI-Safe Questions: Use open-ended prompts to evaluate problem-solving and cultural fit.
5. Perform Technical Whiteboard or Coding Challenge: Simulate on-the-job tasks, focusing on algorithms and system design.
6. Assess Soft Skills: Observe collaboration, adaptability, and communication throughout interviews.
7. Hold a System Design Interview: Evaluate architecture thinking, scalability, and AI integration approaches.
8. Gather Stakeholder Feedback: Debrief with hiring manager and team to align on candidate fit.
9. Check References with Focus on Past AI/Automation Work: Inquire about candidate’s experience working with or alongside AI tools.
10. Make Data-Driven Decision: Use scorecard totals and consensus to select the top candidate.
11. Extend Offer with Clear Timelines: Provide prompt, transparent feedback to maintain candidate engagement.
12. Plan Onboarding with AI Training: Prepare resources to help new hires acclimate to AI-powered workflows.

14. Using Litespace to Improve Your Recruiting Process

Litespace’s AI Recruiting Assistant can help recruiters optimize each stage of the candidate evaluation funnel. By utilizing AI-driven resume screening, recruiters can more efficiently discover top talent based on data-backed ranking, guaranteeing that only the most suitable profiles proceed to interviews. AI pre-screening interviews allow initial assessments of cultural fit, communication, and much more. This can free up recruiters’ time for deeper dives into suitable candidates. During the interview planning portion, Litespace provides structured templates and scorecards tailored to any specific job role, ensuring consistency across candidates. AI-assisted interview note-taking captures critical insights in real time, reducing manual documentation and allowing hiring teams to focus on candidate interactions. This system drives greater efficiency, consistency, and data-driven decision-making.

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, are critical for successful software engineering hires in 2025. By defining rubrics, asking targeted questions, and maintaining consistent processes, recruiters can reduce bias, improve fairness, and achieve better hiring outcomes. Embracing these principles guarantees that candidates are assessed holistically, balancing technical expertise with soft skills, which then results in more adequate teams and stronger organizational performance.