Hire Developers Faster with Voice AI in 2025
Alex Kim
Senior Software Engineer
Alex demonstrated deep understanding of distributed systems and showed excellent problem-solving approach.
Exceptionally clear at explaining complex concepts and collaborating on solutions. Great cultural fit.
Build a stronger tech team
Technical hiring is broken. Generic coding tests miss what matters: problem-solving approach, communication skills, and cultural fit. Our AI-powered conversational interviews go beyond algorithmic puzzles to understand how candidates think, collaborate, and approach real-world engineering challenges.
Traditional tech recruiting struggles with scale, personalization, and candidate experience. Lightscreen's conversational AI solves these challenges by conducting in-depth technical discussions, providing 24/7 candidate support, and delivering data-driven insights that help you identify the engineers who will thrive on your team.
Augment resumes with AI conversations
Go beyond bullet points with dynamic conversations that reveal technical depth, problem-solving approach, and communication skills.
24/7 candidate engagement
Differentiate your company and entice top talent with always-available AI that answers questions and showcases your culture.
Identify the best with data, not vibes
Make confident hiring decisions backed by objective insights, technical assessments, and predictive analytics.
How Lightscreen Transforms Tech Team Hiring
Personalized AI Interviewing
Transform your candidate evaluation process with conversational AI interviews that can go deep into a candidate's previous experience and technical interests. The AI interview augments resumes to help you understand candidate experience, communication skills, and technical skill alignment without adding recruiter workload.
Always-On Candidate Support
Provide instant, accurate answers to candidate inquiries about your company, culture, roles, and processes, keeping them informed and engaged around the clock.
Automated Scheduling
Lightscreen's AI can interview 24/7. Let the candidate call the AI anytime to interview, reducing no-shows and increasing candidate conversion.
I see you built a real-time collaboration platform at your last company. What were the main technical challenges you faced?
The biggest challenge was handling concurrent edits without conflicts. We implemented operational transformation with a custom CRDT solution for eventual consistency.
Interesting choice. Why did you go with CRDTs over other approaches like operational transformation alone?
OT alone required a central server for ordering operations. CRDTs let us build peer-to-peer sync, reducing latency and improving offline support. The trade-off was more complex merge logic.
How did you handle the complexity of CRDT merges? Any specific data structures you found particularly effective?
We used RGA (Replicated Growable Array) for text editing and LWW-Element-Set for metadata. The key insight was keeping operations commutative - made testing much easier.
Your GitHub shows contributions to several open-source projects. How do you balance open-source work with your day job?
I contribute to projects we use at work - it's a win-win. Fixed bugs in our Redis client and added features to a monitoring library. My employer supports it since it benefits our stack.
I see you built a real-time collaboration platform at your last company. What were the main technical challenges you faced?
The biggest challenge was handling concurrent edits without conflicts. We implemented operational transformation with a custom CRDT solution for eventual consistency.
Interesting choice. Why did you go with CRDTs over other approaches like operational transformation alone?
OT alone required a central server for ordering operations. CRDTs let us build peer-to-peer sync, reducing latency and improving offline support. The trade-off was more complex merge logic.
How did you handle the complexity of CRDT merges? Any specific data structures you found particularly effective?
We used RGA (Replicated Growable Array) for text editing and LWW-Element-Set for metadata. The key insight was keeping operations commutative - made testing much easier.
Your GitHub shows contributions to several open-source projects. How do you balance open-source work with your day job?
I contribute to projects we use at work - it's a win-win. Fixed bugs in our Redis client and added features to a monitoring library. My employer supports it since it benefits our stack.
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ATS Integration
Connect with any applicant tracking system.
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Custom formats like live coding and spreadsheets.
Candidate Sourcing
AI agents find and pre-screen qualified candidates.