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Summary of the Role: As 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. 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. Your Contributions to Our Journey: 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 What You Need to Be Successful: Proven Production ML Experience: 6+ 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 Nice to Haves: Experience with AI agents, LLMs, or modern generative AI applications Cybersecurity domain knowledge or experience applying ML to security challenges Background at ML-first companies or organizations where ML was core to the product Experience with modern MLOps practices and cloud-based ML infrastructure Track record of optimizing model performance and controlling AI system costs Why Join Us: Real-World AI Impact: Drive the actual productionization of LLMs and machine learning to solve significant cybersecurity pain points - your work will directly protect organizations from real threats, not just optimize internal metrics Technical Leadership Opportunity: Work directly with our CTO on cutting-edge ML infrastructure while having the autonomy to shape technical decisions and build systems that scale with our hypergrowth Expert Team Partnership: Join a team of hands-on leaders with experience in Big Tech and Scale-ups, including leadership team members who have been part of multiple acquisitions and an IPO Build the AI-Native Future: Shape how generative AI transforms cybersecurity from the ground up, establishing ML practices and technical standards that will define the industry Multiple Growth Pathways: Clear opportunities to grow into Head of ML Engineering, become a domain technical lead, move into customer-facing technical roles, or excel as a senior individual contributor - the choice is yours based on your interests and our needs Breakthrough Technology: Work at the intersection of generative AI and cybersecurity, building solutions that leverage the latest advances in LLMs and AI agents to solve some of the most pressing challenges security teams face today
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.