Esper Software Engineer Interview: Questions & Prep (2026)

Esper builds a device management and orchestration platform for Android-based edge devices. Think kiosks, retail POS terminals, tablets, and IoT gadgets running Android, all managed from one central control plane. Their engineering team works on real-time telemetry, OTA updates, remote configuration, and device security at fleet scale.

As of mid-2026, Esper has 14 open Software Engineer roles. Candidates report a process that typically includes a recruiter screening call, one or two technical rounds covering coding and system design, and a final round with a hiring manager or senior engineer. The company values engineers who understand distributed systems, show curiosity about edge computing, and take strong ownership of their work end-to-end.

Salary bands for Software Engineers in India, from knok jobradar data across 5,395 active openings:

These are market-wide ranges. Esper-specific compensation is best confirmed at the offer stage.

The following questions come up frequently in Esper Software Engineer interviews, based on what candidates report. They span product knowledge, system design, coding, and ownership mindset.

1. Walk us through Esper's product. How does it create value for an enterprise customer?
2. Design a system to collect and process real-time telemetry from a large fleet of Android devices.
3. How would you architect a reliable OTA (over-the-air) update pipeline for edge devices that may have intermittent connectivity?
4. A device goes offline mid-update. What happens to its state, and how do you design for this failure mode?
5. Write a function to parse a device configuration payload and validate that all required fields are present and correctly typed.
6. How do you handle race conditions when multiple backend services try to update the same device state simultaneously?
7. Tell me about a distributed system bug you debugged. What was your investigation process?
8. Design an API that lets third-party developers query device status for fleets of thousands of devices. What would you expose, and how would you handle rate limiting?
9. WebSockets vs. polling for real-time device communication: what are the trade-offs, and which would you choose for Esper's use case?
10. Describe a time you shipped something fast and later had to pay down technical debt. How did you handle it?
11. How do you decide which technical debt to fix in a fast-moving startup environment?
12. A customer reports that some of their devices are not receiving configuration updates. Walk me through your debugging approach step by step.

Q: Design a system to collect real-time telemetry from a large fleet of Android devices.

*Situation:* In my previous role at a logistics company, we needed to track GPS location and battery status from a fleet of delivery tablets spread across multiple cities.

*Task:* I was responsible for designing the data pipeline to ingest, store, and surface this data to operations teams in near real time.

*Action:* I proposed a lightweight MQTT broker layer on the device side to batch and compress events before sending. On the backend, I set up a Kafka cluster to absorb traffic spikes, with separate consumer groups for real-time dashboards and historical storage. I used a time-series database for raw storage and cached recent device state in Redis for low-latency API responses.

*Result:* The system handled peak ingest loads without dropping events, the operations dashboard showed device status with a lag of just a few seconds, and the same architecture was reused for two additional device types without significant rework.

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Q: Tell me about a distributed system bug you debugged. What was your process?

*Situation:* We had a production issue where a small fraction of devices were silently failing to receive remote configuration pushes, with no obvious errors in the main service logs.

*Task:* I needed to find the root cause without taking the system offline, since it was actively serving customers.

*Action:* I correlated error logs across all services in the config push flow, which narrowed the failure to the acknowledgement layer. I added targeted debug logging and traced one failing device end-to-end. The root cause was a timeout misconfiguration in the retry logic: certain devices were silently skipped after a failed first delivery attempt instead of being retried.

*Result:* Fixing the timeout value and adding a dead-letter queue for undelivered messages resolved the issue completely. I also wrote a runbook so the on-call team could identify and fix this class of problem faster in the future.

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Q: Describe a time you shipped something fast and later had to pay down technical debt.

*Situation:* During a tight product launch, I built a device enrollment flow by hardcoding several configuration values to meet the deadline.

*Task:* After the launch, I needed to refactor this safely without breaking any already-enrolled devices.

*Action:* I mapped every place where hardcoded values appeared, built a lightweight configuration service that read values from a central store, and wrote migration tests to verify existing enrollments still worked correctly. I rolled out the change as a canary deployment to catch regressions before full rollout.

*Result:* The refactor was completed in a focused sprint, it eliminated a whole category of environment-specific bugs, and two other teams later extended the configuration service for their own use cases.

STAR for behavioural questions. Every 'tell me about a time' question expects Situation, Task, Action, Result. Keep Situation and Task brief (two to three sentences each). Spend most of your answer on Action, because that is what the interviewer is evaluating. End with a concrete Result, ideally something measurable or with a visible business impact.

Problem decomposition for system design. Candidates report that Esper interviewers appreciate a structured approach: clarify requirements and constraints first, sketch a high-level architecture, then drill into the components that interest the interviewer most. For Esper specifically, always factor in what happens when devices are offline or have flaky connectivity. That is a core constraint in their product domain, and raising it unprompted signals genuine product understanding.

Think out loud for coding rounds. Esper interviewers typically want to see your reasoning, not just a correct solution. State your assumptions, name the data structure you are choosing and explain why, then write the code. If you spot an edge case mid-way, call it out before addressing it.

Product framing for product-knowledge questions. When asked about Esper's product, structure your answer around three things: the customer problem (managing many Android devices at scale is operationally complex), the solution (a central platform for configuration, updates, and monitoring), and the value (reduced downtime, faster deployments, stronger security posture). Showing you understand the business context, not just the technology, stands out.

Ownership over the full lifecycle. Esper is a startup where engineers are expected to care about production behaviour, not just code correctness. Interviewers look for candidates who proactively raise failure modes, monitoring strategies, and rollback plans without waiting to be asked.

Curiosity about edge computing and Android. Prior Android development experience is not required for most backend roles, but candidates report that showing genuine interest in how device constraints shape system design (intermittent connectivity, limited compute, firmware fragmentation) signals a strong cultural fit.

Clear, structured communication. In system design rounds, interviewers want to hear your trade-off reasoning out loud. A candidate who says 'I would choose X over Y because of Z constraint' impresses more than one who jumps straight to a solution without explanation.

Comfort with ambiguity. Startup interviews often involve under-specified problems. Interviewers watch whether you ask the right clarifying questions and state your assumptions explicitly, rather than freezing or guessing silently.

Collaboration signals in past work. In behavioural rounds, Esper interviewers reportedly pay attention to how candidates describe working with cross-functional teams, handling technical disagreements, and unblocking themselves when requirements are unclear or incomplete.

Week 1: Know the product inside out. Use Esper's public documentation, engineering blog, and any available demo content to understand how their device management platform works end-to-end. Be ready to explain the value to a non-technical customer and to describe one or two specific technical challenges their platform must solve.

Week 2: System design for distributed and edge scenarios. Practice designing systems involving message queues, time-series data, real-time protocols (MQTT and WebSockets), and fleet-scale state management. Focus especially on handling offline devices, eventual consistency, and idempotent operations. These themes appear repeatedly in Esper interview reports.

Week 3: Coding fundamentals and language fluency. Candidates report coding rounds that typically involve data structures, JSON parsing and validation, and algorithm problems at a medium difficulty level. Practice in your strongest language and work on explaining your reasoning step by step, not just arriving at an answer.

Week 4: Behavioural prep and mock interviews. Map three to five strong work stories to the STAR format. Cover at least one story each for: debugging a hard production issue, shipping under time pressure, handling a technical disagreement with a teammate, and taking ownership beyond your immediate scope.

If you are actively applying while prepping, knok checks 150+ job sites nightly, applies to roles that match your resume, and messages HR on your behalf, so applications keep going out while you focus on interview preparation.

Not knowing the product. Candidates who cannot explain what Esper does or why managing Android devices at scale is technically hard signal a lack of preparation. Spend time on Esper's website and public documentation before your first round, not after it.

Designing only for the happy path. In distributed and edge device scenarios, interviewers expect you to proactively raise failure modes: what if a device is offline when a config push arrives, what if a message is delivered twice, how do you roll back a bad OTA update? Waiting to be asked about these signals shallow thinking.

Vague STAR answers. Saying 'I worked on improving system performance' without specifics frustrates interviewers. The Action section of your STAR answer should name specific tools you used, decisions you made, and the reasoning behind each choice.

Naming a tool without explaining the trade-off. Saying 'I would use Kafka' is far less impressive than 'I would use Kafka here because we need durable, replayable event storage and ingest can spike unpredictably, but if the team is small and volume is low, a simpler queue might be easier to operate.' Show your reasoning, not just your conclusion.

Ignoring startup context in behavioural answers. Esper is a growth-stage company. Stories that emphasise waiting for detailed specs or working through large approval processes may not land well. Stories about moving fast, making pragmatic trade-offs, and taking initiative tend to resonate more with their interviewers.