Decagon Research Engineer interview questions & guide 2026

Preparing for an interview at Decagon requires a dual focus on rigorous machine learning science and practical, production-grade systems engineering. We look for engineers who possess a deep theoretical understanding of AI/ML but are equally passionate about writing clean, bug-free code that runs reliably at scale.

Role-Related Knowledge – You must demonstrate a comprehensive grasp of LLM architectures, fine-tuning methodologies, and modern retrieval techniques. Be prepared to explain the underlying mathematics of your models as well as the practical nuances of training them.

Problem-Solving & System Design – We value your ability to architect scalable, low-latency systems. When presented with design challenges, focus on latency, cost, reliability, and how you would systematically benchmark your design decisions.

Execution & Momentum – At Decagon, we move fast. You should showcase a track record of end-to-end ownership, demonstrating how you break down highly ambiguous research goals into clear, iterative milestones that deliver immediate value to customers.

Cultural Alignment – We are an in-office team in San Francisco driven by a winner's mindset and a belief that we are stronger together. Be ready to show how you collaborate intensely, embrace feedback, and maintain high standards of excellence.

The interview process at Decagon is designed to evaluate both your technical depth as a researcher and your execution capabilities as an engineer. Because we operate with relentless momentum, our process moves quickly, focusing on real-world engineering challenges rather than purely academic exercises.

You will first speak with a technical recruiter to align on your background, career goals, and our company values. Following this, you will proceed through a series of technical assessments, including deep-dive coding sessions, system design reviews, and discussions regarding your past research and deployment experience. The process culminates in an intensive collaborative onsite interview at our San Francisco office, where you will work alongside our core team to solve problems mimicking our daily engineering challenges.

The timeline above outlines the typical progression from your initial contact to a final offer. Candidates should use this visual guide to pace their technical preparation, focusing heavily on coding and system architecture ahead of the technical screens and onsite rounds. While the exact timing can vary based on candidate availability, we strive to maintain a rapid and transparent feedback loop at every stage.

To succeed at Decagon, you must demonstrate mastery across several key technical and operational domains. Your interviewers will evaluate your depth in these areas through interactive coding, architectural design, and deep-dive technical discussions.

LLM Fine-Tuning & Instruction Tuning

This area evaluates your ability to take base models and turn them into highly specialized, reliable agents capable of executing enterprise-level tasks. You must show that you understand how to curate data, prevent overfitting, and align models to specific behavioral guidelines.

Be ready to go over:

• Dataset engineering – Techniques for filtering, synthetic data generation, and formatting instruction-tuning datasets.
• Alignment algorithms – The practical implementation and comparison of RLHF, DPO, and supervised fine-tuning (SFT).
• Evaluation methodologies – How to build robust internal benchmarks to measure model accuracy, hallucination rates, and task completion.
• Advanced concepts (less common) – Multi-modal fine-tuning, parameter-efficient architectures (PEFT), and custom loss formulation.

Example scenarios:

• "Design a pipeline to fine-tune an open-source model to handle complex billing inquiries while ensuring it never leaks sensitive user data."
• "How would you diagnose and fix a fine-tuned model that has begun to hallucinate API call structures it previously executed correctly?"

High-Performance AI Systems & Retrieval (RAG)

Because our AI agents must resolve customer queries instantly, retrieval latency and accuracy are paramount. This evaluation area focuses on your ability to design and optimize the systems that feed context to our models.

Be ready to go over:

• Vector databases & indexing – Selecting and configuring vector stores for high-throughput, low-latency querying.
• Context optimization – Dynamic context window management, reranking algorithms, and prompt compression techniques.
• Hybrid search architectures – Integrating semantic search with traditional lexical search to handle highly specific customer queries.
• Advanced concepts (less common) – Speculative retrieval, graph-based RAG, and custom embedding model fine-tuning.

Example scenarios:

• "Architect a RAG pipeline that retrieves relevant context from a rapidly updating product catalog of 10 million items, ensuring the model receives fresh data with sub-100ms latency."
• "Propose a system to automatically detect and filter out irrelevant or low-quality retrieved documents before they reach the LLM context window."

ML Production, Scalability & Optimization

We build systems that scale to millions of users. This area tests your ability to deploy models efficiently, drive down inference costs, and optimize latency across various hardware configurations.

Be ready to go over:

• Inference optimization – Deep knowledge of vLLM, TensorRT-LLM, quantization (AWQ, GPTQ), and KV cache management.
• Model serving infrastructure – Designing highly available, auto-scaling model endpoints in cloud environments.
• Monitoring & observability – Setting up guardrails, tracking model drift, and logging inputs/outputs for continuous improvement.
• Advanced concepts (less common) – Custom CUDA kernels, distributed inference partitioning, and multi-tenant model serving optimization.

Example scenarios:

• "Explain how you would optimize a Llama-3-70B model to serve real-time conversational traffic with a target time-to-first-token of under 150 milliseconds."
• "Describe how you would design a fallback architecture for when a primary LLM endpoint experiences an outage or a severe spike in latency."

As a Staff Research Engineer at Decagon, you will own the technical roadmap for our core AI capabilities. Your day-to-day work will bridge the gap between cutting-edge AI research and robust, enterprise-grade software engineering.

• Model Development & Tuning – You will design, train, and deploy custom models specialized in customer support tasks, aiming to exceed the performance of leading closed-source models.
• Latency & Performance Optimization – You will experiment with small, open-source models to drive order-of-magnitude reductions in response latency across chat, email, and voice channels.
• System Architecture – You will build and scale the underlying machine learning systems, retrieval engines, and API integrations that power our conversational agents.
• Cross-Functional Collaboration – Working closely with product managers and core software engineers, you will translate customer needs into technical milestones and robust AI features.
• Technical Leadership – You will mentor other engineers, establish best practices for machine learning code quality, and help guide the strategic direction of our research team.

We are looking for exceptional engineers who have a proven track record of shipping production-grade machine learning systems and thrive in a high-intensity startup environment.

• Must-have skills & experience:

  • 8+ years of professional experience in AI/ML engineering, machine learning research, or closely related systems roles.
  • A proven track record of taking AI/ML projects from initial concept to high-scale production environments.
  • Deep hands-on experience fine-tuning, evaluating, and deploying large language models (LLMs) in real-world applications.
  • Exceptional software engineering skills, with a focus on writing clean, robust, and highly optimized machine learning code.
  • Strong familiarity with modern deep learning frameworks (such as PyTorch) and model serving infrastructure (such as vLLM).

• Nice-to-have skills:

  • Prior experience working with multi-modal models (combining text, voice, and vision).
  • Experience building and optimizing enterprise SaaS architectures or high-throughput conversational agents.
  • Contributions to open-source AI libraries, research publications, or innovative machine learning toolkits.

Q: What is the day-to-day work environment like for the Research team? Decagon is an in-office company based in San Francisco. We believe that physical proximity fosters high-velocity collaboration, rapid iteration, and a stronger team bond. The research team works closely together daily to solve highly complex, real-time engineering challenges.

Q: How does Decagon balance research with production engineering? We do not conduct isolated, academic research. Every research initiative is directly tied to improving our production models, reducing latency, or unlocking new capabilities for our enterprise customers. Our engineers own their models end-to-end, from training to deployment.

Q: What distinguishes a successful candidate for this role? Successful candidates demonstrate a "winner's mindset" and extreme technical autonomy. They are comfortable navigating highly ambiguous problem spaces, writing clean and performant code, and executing rapidly without waiting for perfect specifications.

Q: What technologies do you use most frequently? Our stack leverages cutting-edge machine learning and systems engineering tools. This includes PyTorch, Hugging Face, vLLM, modern vector databases, and highly optimized inference engines, all deployed on scalable cloud GPU infrastructure.

To put your best foot forward during the Decagon hiring process, keep these practical tips in mind:

• Emphasize concrete production impact: When describing your past work, focus heavily on the real-world performance of your models. Highlight metrics such as latency reductions, cost savings, accuracy improvements, and system throughput.
• Showcase end-to-end ownership: Be prepared to explain how you personally designed, built, evaluated, and maintained your models. We value engineers who can dive deep into infrastructure as easily as they can write training loops.
• Demonstrate structured problem-solving: When presented with system design or architectural questions, do not jump straight to a solution. First, gather requirements, state your assumptions, define the constraints (such as latency and budget), and then systematically present your design.

• Align with our core values: We look for candidates who embody our values: customers are everything, relentless momentum, winner's mindset, and stronger together. Frame your behavioral answers to show how you have lived these values in your previous roles.

The Staff Research Engineer role at Decagon offers an unparalleled opportunity to shape the future of conversational artificial intelligence. By joining our team, you will work at the absolute frontier of LLM application, building highly optimized, enterprise-grade systems that solve complex, real-world problems for millions of users daily. The work you do here will directly push the boundaries of what open-source models can achieve under strict production constraints.

As you prepare for your interviews, focus on solidifying your understanding of LLM fine-tuning, low-latency retrieval architectures, and high-performance model serving. Approach every discussion with a focus on ownership, execution velocity, and system scalability.

The compensation range above reflects our commitment to attracting top-tier talent to our high-performing team. In addition to a competitive base salary, Decagon offers substantial equity, comprehensive health benefits, and an in-office environment designed to keep you performing at your absolute best.

For more detailed interview insights, candidate reviews, and preparation resources, explore additional guides and company profiles on Dataford. We look forward to seeing how you can help us drive the next wave of conversational AI innovation. Good luck with your preparation!