Preparing for an interview at Zyphra requires a balanced focus on deep technical execution and high-level system design. You should be ready to explain the "why" behind your technical decisions, not just the "how."

Key Evaluation Criteria

• Technical Depth & Execution – We assess your mastery of your chosen stack, whether that is low-level systems programming, backend API design, or modern frontend frameworks. We look for clean, maintainable, and highly optimized code.
• System Design & Architecture – You must demonstrate the ability to design scalable, fault-tolerant systems. This includes understanding database choices, caching strategies, network protocols, and distributed systems patterns.
• Product & User Empathy – Even in platform roles, we value engineers who care about the end-user experience. You should be able to articulate how your technical decisions impact the developers and customers using our platform.
• Adaptability & Speed – As a fast-growing AI startup, our priorities can shift rapidly. We evaluate your ability to learn new technologies quickly, adapt to new requirements, and deliver high-quality software under tight timelines.

The interview process at Zyphra is designed to evaluate both your practical engineering skills and your ability to collaborate effectively in a high-growth environment. We aim to make the process transparent, rigorous, and respectful of your time.

The journey begins with an initial technical and behavioral screen with a recruiter or hiring manager to align on your background and interest in Zyphra. Following this, you will complete one or two deep-dive technical assessments, which may include live coding, system design, or a practical take-home project depending on your specialization. The final stage is a comprehensive onsite or virtual loop where you will meet with cross-functional team members, dive deeper into architecture, and discuss your alignment with our culture and values.

Throughout this process, we place a strong emphasis on real-world problem-solving rather than academic trivia. We want to see how you think, how you collaborate, and how you handle complex, open-ended engineering challenges.

The timeline above outlines the typical progression from your initial application to the final offer. Candidates should use this roadmap to pace their preparation, focusing first on core coding and system design fundamentals before moving on to deep-dive architecture and behavioral alignment. While the exact duration can vary based on candidate availability, we strive to move through these stages efficiently.

To succeed at Zyphra, you must excel in the specific technical domains relevant to your target role. Below is a detailed breakdown of the primary evaluation areas.

Platform & Infrastructure Engineering

For platform and systems-focused roles, we look for deep expertise in building the infrastructure that powers our AI models. You must understand how to manage compute resources, optimize data pipelines, and ensure high availability.

Be ready to go over:

• Containerization & Orchestration – Deep understanding of Kubernetes, Docker, and managing large-scale clusters.
• Data Pipeline Architecture – Designing scalable data ingestion, storage, and processing pipelines using tools like Kafka, Spark, or similar technologies.
• Infrastructure as Code (IaC) – Managing cloud environments programmatically using Terraform or CloudFormation.
• Advanced concepts (less common) – GPU orchestration, high-performance networking protocols (e.g., InfiniBand), and custom scheduler designs for ML workloads.

Example scenarios:

• "Design an infrastructure setup that can dynamically scale GPU compute nodes based on training queue depth."
• "How would you debug a network latency issue between a distributed database cluster and our inference API?"

Full-Stack & Frontend Architecture

For full-stack and frontend roles, the focus is on building seamless, highly performant interfaces that make complex AI capabilities intuitive for developers and enterprise clients.