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# Machine Learning Engineer

Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move.

[Apply for this job](#job-application)[View company](https://jobicy.com/company/maze.md)Share24 Aug 2026Published30Listing views1Application actions23 Sep 2026Apply before  Opportunity details

## About this role.

AI SummaryMaze is hiring a senior Machine Learning Engineer to lead the productionization of LLM- and agent-based cybersecurity capabilities. The role owns evaluation frameworks, ML experimentation-to-production pipelines, monitoring, and integration of ML features into the customer product. It requires at least six years of production ML experience, strong Python and deep-learning foundations, and the ability to work directly with the CTO while operating independently. The engineer will improve agent performance, build scalable MLOps foundations, and mentor junior engineers through technical leadership.

## Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

### Job Complexity

5/5EasyHard

### Pace & Pressure

5/5RelaxedFast-paced

### Autonomy Level

5/5GuidedFull ownership

### Communication Load

4/5IndependentCollaborative

AI insightThis is a high-seniority, early-stage technical leadership role requiring end-to-end ownership of production ML infrastructure and measurable improvements to AI-agent performance. The successful candidate must balance hands-on engineering, product integration, reliability, experimentation, and cybersecurity use cases in a fast-moving startup environment.

## Salary analysis

Estimated compensation compared with the broader UK market for similar roles.

Estimated job medianHighly competitive£117,500UK market range£100k–£135k0£149k

AI insightThe disclosed annual salary range is £100,000–£135,000, with a midpoint of £117,500. This is a competitive UK-market range for a senior Machine Learning Engineer with production LLM, MLOps, and technical-leadership responsibilities, particularly within an AI-focused cybersecurity startup.

## Core skills

Skills and capabilities most closely associated with this opportunity.

[Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[LLMs](https://jobicy.com/jobs?search_keywords=LLMs.md)[AI Agents](https://jobicy.com/jobs?search_keywords=AI%20Agents.md)[MLOps](https://jobicy.com/jobs?search_keywords=MLOps.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[Deep Learning](https://jobicy.com/jobs?search_keywords=Deep%20Learning.md)[Evaluation Frameworks](https://jobicy.com/jobs?search_keywords=Evaluation%20Frameworks.md)[ML Infrastructure](https://jobicy.com/jobs?search_keywords=ML%20Infrastructure.md)[Prompt Engineering](https://jobicy.com/jobs?search_keywords=Prompt%20Engineering.md)[Cybersecurity](https://jobicy.com/jobs?search_keywords=Cybersecurity.md)

Sample interview questionsDescribe a production ML system you took from experimentation to customer deployment. What were the key technical and operational decisions?I would explain the initial customer problem and success metrics, then cover dataset and model choices, offline evaluation, deployment architecture, monitoring, rollback controls, and iteration process. I would quantify production outcomes such as latency, reliability, adoption, model-quality improvement, or business impact.

How would you design an evaluation framework for an AI agent used in cybersecurity workflows?

I would define task-specific success criteria, including correctness, safety, groundedness, latency, cost, and analyst usefulness. I would combine curated adversarial test sets, production traces, automated scoring where reliable, human review for high-risk cases, versioned benchmarks, and dashboards that compare agent versions over time.

How do you balance rapid experimentation with reliability in an early-stage ML product?

I would separate fast prototyping from controlled production release paths. Feature flags, reproducible experiments, versioned prompts and models, automated regression evaluations, observability, staged rollouts, and clear rollback procedures allow teams to move quickly without exposing customers to uncontrolled quality or security risks.

What approach would you take to improving an underperforming LLM agent?

I would first segment failures using traces and evaluation data to identify whether the issue is retrieval, prompting, tool use, model selection, workflow design, or ambiguous task definition. I would test focused hypotheses, measure improvements against a fixed benchmark and live guardrail metrics, then deploy the smallest validated change through a staged rollout.

How do you communicate ML trade-offs to product leaders and customers who may not be deeply technical?

I translate technical choices into user outcomes, risks, and measurable trade-offs such as accuracy, response time, cost, coverage, and failure modes. I use concrete examples, establish agreed success metrics early, and clearly state what the system can and cannot reliably do so stakeholders can make informed product decisions.

### 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:

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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

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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

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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

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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

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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

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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

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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:

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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

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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

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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

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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

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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

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Self-Directed Leadership: Ability to operate autonomously while maintaining close alignment with leadership, comfortable with frequent check-ins but capable of driving projects independently

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Cross-Functional Collaboration: Experience working closely with product teams and potentially customers, translating technical capabilities into business value and user experiences

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Nice to Haves:

Experience with AI agents, LLMs, or modern generative AI applications

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Cybersecurity domain knowledge or experience applying ML to security challenges

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Background at ML-first companies or organizations where ML was core to the product

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Experience with modern MLOps practices and cloud-based ML infrastructure

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Track record of optimizing model performance and controlling AI system costs

### Why Join Us:

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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

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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

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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

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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

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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

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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

Show more

[Apply now >](https://jobicy.com/jobs/151465-machine-learning-engineer-6.md)

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