About this role.
Maze is hiring a senior Backend Engineer to build and scale AI-agent and machine-learning systems for cybersecurity use cases. The role owns backend architecture, REST APIs, database systems, LLM/ML integrations, and production reliability across the development lifecycle. It requires at least seven years of backend experience, strong Python-oriented server-side development, and familiarity with AWS, CI/CD, Docker, and Kubernetes. The engineer will work closely with product and design, prototype quickly in a startup environment, and increasingly mentor junior engineers and guide technical standards.
Role DNA
A quick view of the complexity, pace, ownership and collaboration implied by the job description.
Job Complexity
5/5Pace & Pressure
5/5Autonomy Level
5/5Communication Load
4/5Salary analysis
Estimated compensation compared with the broader EU market for similar roles.
Core skills
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Sample interview questions
I would separate synchronous API handling from asynchronous orchestration, using durable job queues and persisted workflow state. Each tool invocation would be idempotent, observable, and governed by retries, timeouts, and dead-letter handling. I would also add audit logs, permission boundaries, and evaluation telemetry so agent outcomes can be inspected and improved safely.
I would prioritize data isolation, access control, prompt-injection defenses, output validation, and careful treatment of sensitive customer data. The system should use structured outputs where possible, enforce tool permissions server-side, and retain traceability for decisions and actions. I would also monitor quality, latency, cost, and model failure modes through production evaluations.
I would first establish metrics for latency, throughput, error rate, queue depth, database performance, and downstream model calls. Based on bottlenecks, I would introduce caching, horizontal worker scaling, connection pooling, asynchronous processing, database indexing, and rate limits. I would validate each change with load testing and define service-level objectives to prevent regressions.
I would keep prototypes narrow and measurable while establishing non-negotiable guardrails such as code review, automated tests for critical paths, secret management, logging, and deployment checks. Once an experiment proves valuable, I would schedule deliberate hardening work for architecture, documentation, and operational readiness. This enables speed without allowing temporary shortcuts to become permanent production risk.
I would use clear task scoping, design discussions, constructive code reviews, and pairing on complex work to build both context and confidence. I would explain trade-offs rather than only prescribing solutions, then gradually increase ownership as engineers demonstrate readiness. Shared coding standards, documentation, and regular feedback make mentoring scalable while improving team delivery quality.
Summary of the Role:
As an Engineer at Maze, you’ll play a pivotal role in shaping our product, with significant focus on the development of AI Agents and ML systems. You will have the unique opportunity to design, build, and scale a product from the ground up, influencing everything from architecture decisions to ML integrations and the overall user experience. This is an exciting chance to be at the core of our technical team, driving innovation in AI-powered cybersecurity solutions and ensuring seamless integration across the stack.
Your Contributions to Our Journey:
AI Agents: Play a pivotal role in the development and scaling of complex AI Agents to tackle some of the biggest challenges in cybersecurity.
Architect and Develop: Design and implement backend systems that effectively support agentic workloads, ensuring they are scalable, maintainable, and secure.
End-to-End Ownership: Take ownership of the entire development lifecycle, from concept and design to deployment and maintenance.
Integrate Seamlessly: Work closely with other engineers, our designer/product manager to ensure seamless integration of new features and services.
Optimize for Performance: Continuously monitor and improve application performance, security, and scalability.
Establish Best Practices: Define and enforce coding standards, best practices, and documentation to maintain high code quality.
Rapid Prototyping: Quickly prototype and iterate on new features, adapting to user feedback and changing requirements.
Mentor and Lead: As the team grows, mentor junior engineers and lead by example in technical discussions and code reviews.
What You Need to Be Successful:
Extensive Experience: 7+ years of experience in backend development
Backend Mastery: Strong experience with backend development, including RESTful API design, database management, and server-side frameworks (e.g., Python).
ML/AI Understanding: Working knowledge of machine learning principles and experience integrating LLMs or other AI services into production applications. Familiarity with tools like LangChain, LlamaIndex, or similar frameworks is a plus.
Cloud Experience: Familiarity with cloud platforms (e.g., AWS) and their ML services, along with DevOps practices, including CI/CD and containerization (e.g., Docker, Kubernetes).
Problem-Solving Skills: Strong analytical and problem-solving abilities, with a focus on delivering robust and scalable solutions.
AI Systems Architecture: Understanding of how to architect systems that effectively leverage AI capabilities while maintaining performance and reliability.
Collaborative Spirit: Excellent communication skills and the ability to work effectively in a cross-functional team.
Agility and Adaptability: Comfort working in a fast-paced startup environment with the ability to pivot and adapt as needed, particularly in the rapidly evolving AI landscape.
Why Join Us:
Ambitious Challenges: We are using Generative AI (LLMs and Agents) to solve some of the most pressing challenges in cybersecurity today. You’ll be working at the cutting edge of this field, aiming to deliver significant breakthroughs for security teams.
Expert Team: We are a team of hands-on leaders with deep experience in Big Tech and Scale-ups. Our team has been part of the leadership teams behind multiple acquisitions and an IPO.
Impactful Work: Cybersecurity is becoming a challenge to most companies and helping them mitigate risk ultimately helps drive better outcomes for all of us.
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