Decagon Software Engineer interview questions & guide 2026

Interview questions at Decagon reflect real-world technical challenges encountered when building low-latency, stateful conversational systems. Rather than focusing purely on abstract textbook theory, interviewers evaluate your ability to handle real-time data constraints, state mutation, asynchronous processing, and large-scale system design.

#### Practical Coding & Data Structures

This category evaluates your ability to design robust, clean, and thread-safe data structures in memory. Interviewers place heavy emphasis on sliding-window mechanisms, state tracking, and handling out-of-order execution.

- **Design a Time-Window CSAT Tracking Class**: Create a class initialized with a time window size. Implement methods to record a unique conversation ID, score (1–5), and timestamp. Support returning the average score in the active window, handling out-of-order timestamps, and updating scores for active conversations.
- **Implement a Sliding Window Metric Aggregator**: Design a sliding-window data structure supporting `insert(id, value, timestamp)`, `get_avg()`, and `update(id, value)`. Ensure that expired records are evicted on operations and handle out-of-order arrivals cleanly.
- **The Skyline Problem**: Given a set of rectangular buildings, return the critical points that define their outer outline. Be prepared to explain boundary conditions and algorithmic efficiency.
- **Read File via Multithreading**: Write a concurrent execution routine in Python or TypeScript that reads and processes file segments using thread pools or worker threads, ensuring memory efficiency and race-condition safety.

#### Object-Oriented & State Management

These questions assess your ability to design extensible, clean software architectures using solid domain modeling and design patterns.

- **Build a Cooking App Shopping Cart**: Implement an object-oriented shopping cart supporting operations like `add_recipe(name)`, `remove_recipe(name)`, and `get_total_discount()`. Extend the state machine to support full transactional `undo()` and `redo()` capabilities.
- **Replicate a Stateful Component in React**: Construct an interactive UI component that maintains local state, communicates with backend APIs asynchronously, and manages edge cases like optimistic UI updates and error boundaries.
- **SOLID Principles in Production**: Explain how you apply SOLID principles to decompose monolithic workflows into modular, testable components within asynchronous runtime environments.

#### System & ML Architecture Design

System design at **Decagon** focuses heavily on AI agent orchestration, real-time message passing, and tenancy models.

- **Design a Single-Tenant Chatbot Platform**: Architect an isolated customer service agent for a single client and domain. Detail the ingestion pipeline, context retrieval, dialogue state management, and real-time execution flow.
- **Design an Enterprise AI Gateway**: Architect a unified gateway for dynamic model routing, fallback handling, rate-limiting, and cost tracking across multiple LLM providers (e.g., OpenAI, Anthropic, internal models).
- **Design Slack / Messaging System**: Build a scalable, real-time messaging architecture supporting multi-channel delivery, presence, and persistence over WebSockets.

#### System Fundamentals & Data Infrastructure

This area checks your knowledge of core operating system concepts, concurrency, and persistent storage trade-offs.

- **Relational vs. Non-Relational Databases**: Compare relational storage models against NoSQL stores for real-time conversation state tracking and long-term telemetry storage.
- **Low-Latency Logging & ETL Pipeline**: Design an ETL architecture using Kafka, Spark/Flink, and persistent storage to process telemetry data while maintaining tight p99 observability.

Preparing for Decagon requires balancing algorithmic precision with real-world system engineering. You must demonstrate clean code, deep architectural understanding, and an execution mindset tuned to startup velocity.

Role-Related Technical Depth – You are expected to demonstrate complete mastery over your primary language (Python, TypeScript, C++, or Go). Interviewers assess your knowledge of asynchronous programming, concurrency constructs (multithreading, event loops), object-oriented principles, and low-level data structures.

Problem-Solving & State Management – Decagon heavily tests your ability to model complex, stateful applications. Candidates must showcase structured thinking when handling messy real-world inputs, such as out-of-order timestamps, state rollbacks (undo/redo), and sliding-window invalidations.

System & Agent Architecture – For design rounds, you must navigate distributed systems concepts with ease. You need to articulate how to build reliable execution engines, manage multi-provider LLM routing, design event-driven streaming pipelines (Kafka, Flink), and enforce strict SLOs for low-latency voice and chat streams.

Startup Culture & Momentum Fit – Decagon operates with high intensity and an in-office culture centered around shared ownership and rapid shipping. Evaluators check if you thrive in ambiguous environments, take complete end-to-end ownership of projects, and embody a customer-first, high-velocity mindset.

The Decagon software engineering interview process is designed to test practical execution over theoretical puzzles. The hiring loop is rigorous, fast-paced, and closely aligned with the actual technical problems faced by the team. Decisions are often communicated quickly, reflecting the company's focus on momentum.

The journey begins with an initial recruiter screening to evaluate your background, career trajectory, and alignment with Decagon's high-velocity, in-office culture. Following this, candidates go through technical screening calls conducted via platforms like CoderPad. These screens emphasize practical live coding—such as building time-windowed metrics calculators or implementing concurrent file processing—alongside system design evaluations.

Candidates who pass the screening phase enter the comprehensive onsite loop. The onsite consists of multiple technical deep-dives covering practical data structures, stateful application coding, system and ML architecture design, and behavioral/experience discussions.

The timeline module above maps out the typical progression from initial outreach to the final offer. Candidates should note that technical screens often feature same-day or next-day feedback. Prepare to sustain high technical energy across the multi-stage onsite loop.

Practical Data Structures & Sliding Windows

This area evaluates your capability to handle stateful, real-time data manipulation in memory under time constraints. Decagon's AI agents generate continuous streams of telemetry, metrics, and conversation events that must be aggregated without sacrificing runtime efficiency.

Be ready to go over:

• Sliding Window eviction strategies – Invalidating expired records dynamically during insert or get_avg calls using double-ended queues (deques) or dynamic arrays.
• Out-of-order timestamp processing – Handling out-of-sequence events gracefully while maintaining sorted indices or ring buffers.
• Rolling aggregations and updates – Computing running sums, averages, or percentiles in $O(1)$ or $O(\log N)$ amortized time.
• Advanced concepts (less common) – Treaps or Segment Trees for range-query modifications over custom time intervals; dynamic percentile estimation using t-digests.

Example scenarios:

• "Design a CSAT tracker class with fixed score bounds (1-5) that supports out-of-order timestamp insertions and dynamic score updates for active conversations within a rolling 60-minute window."
• "Implement a sliding-window moving average class supporting insert, update, and get_avg operations, ensuring that stale entries are pruned efficiently upon every read and write."

Stateful Application & API Design

This evaluation area tests your ability to translate domain requirements into clean, object-oriented codebases. Candidates must write modular, maintainable, and extensible code while managing complex local state and dynamic operational history.

Be ready to go over:

• State Machine & Command Pattern – Structuring operations to support dynamic state mutation, rollback (undo), and re-application (redo).
• Object-Oriented Abstractions – Applying SOLID principles to encapsulate logic across service classes, models, and controllers.
• Concurrency & Multithreading – Managing shared mutable state safely across concurrent execution threads or asynchronous event loops.
• Advanced concepts (less common) – Operational Transformation (OT) or Conflict-Free Replicated Data Types (CRDTs) for collaborative client-side editing state.

Example scenarios:

• "Implement an API for a recipe shopping cart supporting dynamic recipe additions, quantity-based discount applications, and a complete transactional undo/redo stack."
• "Write a multi-threaded file processor in Python or TypeScript that parses log entries concurrently without creating data races or memory leaks."

System & Agent Architecture Design

This domain assesses your capacity to design distributed, high-throughput, and low-latency systems. Decagon builds complex conversational pipelines, requiring engineers to design flexible agent execution graphs, multi-tenant/single-tenant isolation layers, and resilient infrastructure.

Be ready to go over:

• LLM Agent Orchestration – Building execution engines that manage agent reasoning loops, tool calling, and multi-provider fallback routing.
• Real-Time Data Pipelines – Architecting streaming pipelines using Kafka, Spark, or Flink for telemetry ingestion and live observability.
• Tenancy & Isolation Models – Designing dedicated single-tenant environments versus scalable multi-tenant deployments with strict data perimeter security.
• Advanced concepts (less common) – Telephony integration via WebRTC/SIP gateways; sub-hundred-millisecond audio streaming pipelines for real-time voice agents.

Example scenarios:

• "Architect a single-tenant conversational agent system that processes multi-channel incoming customer requests, coordinates external API tool execution, and logs conversation context under strict latency budgets."
• "Design an AI Gateway that dynamically routes model requests across OpenAI, Anthropic, and self-hosted models while managing rate limits, fallback strategies, and cost analytics."

As a Software Engineer at Decagon, your day-to-day work centers on designing, deploying, and operating critical production infrastructure that powers autonomous AI agents. You will take full end-to-end ownership of engineering deliverables, working across the entire stack from core execution runtimes down to cloud infrastructure.

In your day-to-day role, you will:

- **Build Agent Orchestration Runtimes**: Construct low-latency, resilient workflow engines that manage how AI agents make decisions, invoke external APIs, and execute actions in real time.
- **Optimize Infrastructure & Latency**: Tune networking paths, optimize database queries, implement multi-layer caching, and optimize system pipelines to satisfy strict p95 and p99 latency SLOs.
- **Develop Core Data Pipelines**: Design real-time streaming architectures using Kafka, Spark, or Flink to capture telemetry, drive analytics, and support live evaluation pipelines.
- **Maintain Operational Excellence**: Author Terraform scripts, manage Kubernetes deployments across cloud environments (AWS, GCP), participate in on-call rotations, and create self-healing infrastructure paths.
- **Cross-Functional Collaboration**: Partner directly with AI/ML researchers, product managers, and customer-facing teams to translate customer requirements into production-ready software features.

Candidates applying for the Software Engineer role at Decagon are expected to bring deep technical fundamentals combined with a strong execution bias.

Must-Have Skills

• Core Engineering Proficiency: 5+ years (mid/senior level) or 8+ years (staff level) of experience building scalable production systems using Python, TypeScript, Go, or C++.
• Asynchronous & Concurrent Programming: Deep understanding of event loops, asynchronous runtimes, multithreading, and memory-safe concurrent data access.
• Distributed Systems Design: Proven ability to architect fault-tolerant distributed systems, manage state across distributed nodes, and debug complex production stacks.
• Data Structures & Algorithmic Rigor: Mastery of foundational algorithms, time/space complexity analysis, and custom memory-resident data structure design.

Nice-to-Have Skills

• AI/LLM Infrastructure Experience: Prior experience building workflow engines, agentic runtimes, or multi-provider model routing gateways.
• Real-Time Streaming Stack: Hands-on familiarity with streaming engines (Apache Kafka, Flink, Spark) and low-latency storage engines.
• Cloud Infrastructure & Kubernetes: Experience with Infrastructure-as-Code (Terraform), Kubernetes (EKS/GKE), and managing multi-cloud or customer-managed deployments.
• Voice & Telephony: Familiarity with WebRTC, SIP protocols, or low-latency audio processing pipelines.

Q: How difficult are the coding rounds at Decagon? The coding questions focus primarily on practical data structure design, sliding-window algorithms, concurrency, and clean object-oriented architecture rather than abstract dynamic programming. However, expect strict time limits and follow-up constraints like handling out-of-order data or concurrency.

Q: Does Decagon allow remote work for software engineering roles? Decagon is primarily an in-office company (with key hubs in San Francisco and New York) focused on in-person collaboration and fast execution. While remote interviews are supported, candidates should expect in-office expectations unless hired for specifically remote-designated positions.

Q: What sets successful candidates apart during the system design rounds? Successful candidates demonstrate clear familiarity with modern AI agent stacks (orchestration, state management, tool execution) alongside standard distributed systems knowledge. They proactively address edge cases like latency budgets, fallback strategies, and data tenancy options.

Q: How quickly does Decagon make hiring decisions? Decagon moves fast. Candidates frequently receive feedback or invitations to subsequent interview stages on the same day or within 24 to 48 hours of completing a round.

• Master Sliding Windows and Out-of-Order Execution: Be ready to code custom sliding-window data structures from scratch. Practice implementing clean eviction logic and managing dynamic score or metric updates with un-ordered timestamps.
• Emphasize End-to-End Ownership: Decagon values engineers who take projects from initial design through deployment, telemetry, and operational support. Highlight times you owned projects end-to-end in your behavioral discussions.
• Structure System Design Answers around AI Workflows: When designing systems, account for the unique latency and reliability constraints of LLM inference, dynamic tool execution, dynamic context retrieval, and model failovers.
• Practice Clean Object-Oriented Code Under Time Pressure: Ensure your live coding demonstrates clean abstractions, meaningful variable names, and clear separation of concerns (such as implementing the Command pattern for undo/redo functionality).

Joining Decagon as a Software Engineer offers a unique opportunity to build core infrastructure at the leading edge of enterprise AI conversational platforms. You will tackle demanding technical challenges—from low-latency agent orchestration and real-time streaming architectures to voice infrastructure and multi-tenant AI systems—working alongside a talented team in a high-velocity environment.

To succeed in the interview process, focus your preparation on practical data structure design, sliding-window algorithms, concurrency, object-oriented state management, and real-time system architecture. Demonstrating strong execution, clear communication, and an ownership mindset will make you stand out throughout the process.

Candidates looking to deepen their interview preparation can explore detailed technical breakdown guides, real candidate interview reports, and interactive practice sets on Dataford.

The compensation module above reflects total target compensation ranges across software engineering levels at Decagon. Offers vary based on seniority, specialization (such as Core Infrastructure vs. Agent Orchestration), and prior industry experience, combining competitive base salaries with equity packages aligned with the company's growth trajectory.