Cresta Software Engineer Interview: Questions & Prep (2026)

Cresta builds AI-powered real-time assistance for contact centre agents, helping them respond better during live customer conversations. The technology sits at the intersection of large language models, real-time transcription, and applied ML, which shapes exactly what the engineering interview tests.

As of July 2026, Cresta has 111 open Software Engineer roles across product, platform, and machine learning teams. Candidates report a process that typically runs 3-5 rounds: a recruiter call, one or two coding rounds focused on data structures and algorithms, a system design round, and a behavioural conversation. Engineers applying to ML-adjacent teams may also face a round specifically on model concepts and ML system trade-offs.

Cresta is remote-friendly, so expect video interviews throughout. Most candidates report completing the full loop in 2-3 weeks, though timelines can shift depending on team availability and the specific role.

These questions reflect Cresta's core engineering focus areas: real-time AI systems, low-latency architecture, NLP pipelines, and cross-functional collaboration. All sourced from candidate reports.

1. Walk us through how you would design a real-time AI suggestion system that surfaces recommendations to a contact centre agent within a tight latency budget.
2. How do you reason about the trade-off between model accuracy and inference latency in a production environment?
3. Tell us about a time you built or optimised a system that processed streaming or event-driven data at scale.
4. How would you debug a latency spike in a live service handling thousands of concurrent requests?
5. Describe your experience with NLP pipelines or large language models. How have you moved a model from experiment to production?
6. How would you design the data pipeline that feeds a real-time AI layer receiving live call transcriptions?
7. How do you write code that other engineers can read, maintain, and extend two years from now?
8. Describe a time a system you owned failed in production. What happened, and what did you change afterwards?
9. How do you decide when to use an off-the-shelf model versus fine-tuning or building one from scratch?
10. Tell us about a disagreement you had with a product manager or data scientist about what to prioritise. How did it resolve?
11. How do you keep up with fast-moving developments in AI and NLP, and how do you decide what is worth adopting?
12. What draws you to working on AI for customer conversations, and how do you see this space evolving over the next few years?

Use these as a structure guide and replace the specifics with your own experience.

Q: Tell us about a time you built or optimised a system that processed streaming data at scale.

*Situation:* At my previous company, our event processing pipeline handled user activity streams from a mobile app. As the user base grew, the pipeline started dropping events during traffic spikes, causing gaps in our analytics.

*Task:* I was asked to redesign the ingestion layer to handle a significantly higher load without data loss, while keeping end-to-end latency under a few seconds.

*Action:* I audited the existing Kafka consumer setup and found our consumer group had too few partitions and our processing logic was doing synchronous DB writes per event. I repartitioned the topic, rewrote the consumer to batch writes, added a dead-letter queue for failed events, and set up lag monitoring dashboards so the on-call team could react quickly.

*Result:* The pipeline handled the next traffic peak without drops. The on-call alert count fell noticeably in the following quarter, and the batching pattern we introduced became the team standard.

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Q: Describe a time a system you owned failed in production.

*Situation:* A caching layer I had built for a recommendation service started returning stale data to users after a deployment. The issue went undetected for several hours because our alerting only watched error rates, not data freshness.

*Task:* I needed to restore correct behaviour quickly, identify the root cause, and prevent a recurrence.

*Action:* I rolled back the deployment within minutes of detection, then spent the next day tracing the bug to a cache invalidation race condition introduced by a config change. I added a freshness-check metric and alert, wrote a post-mortem, and updated our deployment checklist to include a cache-state validation step.

*Result:* We had no repeat of the issue. The post-mortem was shared across teams and led to a broader audit of our alerting coverage, which caught two other blind spots.

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Q: Tell us about a disagreement you had with a product manager about what to prioritise.

*Situation:* A product manager wanted to ship a new feature on a tight deadline. I flagged that the underlying data model had a scalability issue that would cause problems as usage grew.

*Task:* I needed to make the case for addressing the technical debt without blocking the product roadmap entirely.

*Action:* I prepared a short document showing the projected failure point based on our current growth trajectory and proposed a phased plan: ship the feature with a temporary safeguard, then fix the data model in the following sprint. I framed it as risk reduction rather than a blocker.

*Result:* The PM agreed to the phased plan. We shipped on time, and the data model fix landed in the next sprint as agreed. The feature scaled without issues.

For system design questions: Clarify scale and constraints before drawing any architecture. Candidates who jump to solutions without asking questions often design for the wrong problem. Walk through requirements, rough capacity estimates, data flow, component choices, and failure modes. For Cresta specifically, bring latency into the conversation early since their product is real-time.

For coding questions: Think aloud before writing. State the brute-force approach first, then reason through optimisation. Ask about edge cases: empty input, duplicates, very large inputs. Clean up variable names before you declare the solution done.

For behavioural questions: Use the STAR format: Situation, Task, Action, Result. Keep the Situation brief (two or three sentences). Spend most of your time on the Action, since that is where interviewers see your reasoning. End with a concrete Result and, if possible, what you learned or changed afterwards.

For ML trade-off questions: Structure your answer around three axes: accuracy, latency, and cost. Show that you understand the product context (a contact centre agent needs a suggestion in seconds, not minutes) and can translate that into architectural decisions.

Comfort with trade-offs, not just solutions. Cresta's engineering challenges involve real-time constraints, ML uncertainty, and distributed systems all at once. Interviewers want to see you reason through competing forces, not just pattern-match to a standard architecture.

Ownership mindset. Candidates report that Cresta values engineers who treat a broken system as their problem to fix, not someone else's. Stories where you spotted an issue proactively and drove it to resolution stand out more than stories where you were assigned a task and completed it.

Clear, direct communication. Because Cresta teams work across ML, product, and infrastructure, interviewers look for engineers who can explain a technical decision to a non-technical stakeholder without losing precision.

Genuine curiosity about AI. The company is building in a fast-moving space. Interviewers notice when a candidate has thought about the domain beyond the job description, for example how LLM capabilities are changing what is possible in real-time conversation analysis.

Week 1: Foundations
Review core data structures and algorithms with a focus on arrays, hash maps, trees, and graphs. Practise problems that involve streaming or sliding-window patterns, since these mirror Cresta's domain. Read about event streaming systems such as Kafka if you have not worked with them.

Week 2: System design and ML
Practise two or three system design problems focused on real-time or high-throughput scenarios. Study the components of an ML inference pipeline: feature stores, model serving, latency budgets, and monitoring. Read Cresta's engineering blog and any public talks from their team to understand how they think about their architecture.

Week 3: Behavioural prep and mock interviews
Write out several stories from your own work using the STAR format. Cover a technical failure you owned, a cross-functional disagreement, a system you designed from scratch, and a time you improved something that was not your direct responsibility. Do at least two mock interviews with a friend or on a practice platform so you hear yourself explain things aloud.

Before each round: Re-read the job description and map your prepared stories to the likely topics for that specific round.

Not knowing what Cresta's product does. Several candidates report being caught off guard when asked 'why Cresta?' If you cannot describe what the product does and why the engineering is interesting, it signals low motivation.

Jumping into system design without clarifying constraints. Interviewers at AI-focused companies frequently test whether you ask the right questions before designing. Starting to sketch solutions without discussing scale, latency targets, or failure modes is a common early mistake.

Weak results in behavioural stories. Ending a STAR answer with 'the team was happy' or 'it went well' leaves interviewers without signal. Always describe the outcome concretely, for example 'reduced alert volume by roughly half' or 'the feature shipped a week ahead of schedule.'

Over-indexing on ML theory without production context. Cresta is an applied AI company. Answers that stay at the level of model architecture without connecting to latency, cost, or deployment reality often miss the mark.

Not asking questions at the end. Cresta interviewers typically leave time for your questions. Candidates who say 'I think you covered everything' can seem disengaged. Prepare two or three specific questions about the team's current engineering challenges or how they handle model updates in production.