Senior Machine Learning Engineer, Maze

• This role is perfect for a hands-on ML engineer who has scaled production ML systems across multiple companies, thinks like a product builder, and wants to drive the actual productionization of LLMs and ML to solve significant pain points
• Proven Production ML Experience: 5+ years building and scaling machine learning systems in production environments, with hands-on experience moving from experimentation to customer-facing deployments
• Deep Neural Networks Foundation: Strong background in classical neural networks and deep learning fundamentals before specializing in modern LLMs and transformer architectures - you understand the foundations, not just the latest tools
• Product-Focused ML Mindset: Experience building ML systems that solve real business problems, with a track record of integrating classification, prediction, or recommendation systems into actual products customers use
• Multi-Company Perspective: Experience across multiple organizations (scale-ups, startups, or combination), giving you practical knowledge of what tools to build vs buy and how to avoid over-engineering
• Technical Versatility: Strong Python skills with flexibility across ML frameworks and tools - comfortable adapting to our stack including LangChain, evaluation frameworks, and workflow orchestration tools like Temporal
• Self-Directed Leadership: Ability to operate autonomously while maintaining close alignment with leadership, comfortable with frequent check-ins but capable of driving projects independently
• Cross-Functional Collaboration: Experience working closely with product teams and potentially customers, translating technical capabilities into business value and user experiences

Desirable

• As Senior ML Engineer at Maze, you'll be the technical leader driving our machine learning infrastructure from experimentation to production, ensuring our AI-powered cybersecurity solutions deliver measurable impact for customers worldwide
• This is a unique opportunity to join as one of the early engineering team members of a well-funded startup building breakthrough applications of LLMs and AI agents in cybersecurity
• You'll take full ownership of evaluation frameworks, production ML pipelines, and cross-team ML integration, working closely with our CTO and product teams to transform cutting-edge AI research into robust, scalable solutions that solve real security challenges
• Your success will be measured by agent performance improvements and product innovation impact, not just technical metrics
• Build Production-Grade Evaluation Systems: Design and implement comprehensive evaluation frameworks that measure agent performance, track improvements over time, and ensure our AI systems deliver consistent value to customers
• Drive Experimentation-to-Production Pipeline: Own the entire ML lifecycle from prototype to production, building scalable systems that enable rapid iteration while maintaining reliability and performance in customer environments
• Enable Cross-Team ML Integration: Work closely with product teams to seamlessly integrate ML capabilities into customer-facing features, ensuring technical excellence translates into user value and product differentiation
• Optimize AI Agent Performance: Continuously improve our AI agents through systematic experimentation, prompt engineering, and architectural enhancements, measuring success through customer impact and system performance
• Scale ML Infrastructure: Build the foundational ML systems, monitoring, and tooling that will support our growth from startup to scale, ensuring we can deploy new capabilities quickly without compromising quality
• Partner with Engineering Leadership: Collaborate directly with our CTO through regular check-ins and strategic alignment while operating with high autonomy and self-direction in day-to-day execution
• Mentor Through Excellence: Provide natural mentorship to junior ML engineers through code reviews, technical guidance, and sharing practical experience from building production ML systems