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Sundayy

Machine Learning Engineer

England
Posted about 16 hours ago
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About The Company

Maze is an innovative startup dedicated to advancing cybersecurity through cutting-edge artificial intelligence solutions. Backed by substantial funding, Maze is building groundbreaking applications of large language models (LLMs) and AI agents to address complex security challenges faced by organizations worldwide. Our mission is to leverage the power of AI to create scalable, reliable, and impactful cybersecurity tools that protect digital assets and ensure operational resilience. We foster a dynamic, collaborative environment where technological excellence and creative problem-solving are paramount, enabling our team members to make meaningful contributions to the future of cybersecurity technology.

About The Role

We are seeking a highly skilled Machine Learning Engineer to join Maze as a key technical leader in our AI and cybersecurity initiatives. In this role, you will drive the development and deployment of our machine learning infrastructure, overseeing everything from experimental prototypes to production-ready systems. As one of the early engineering team members, you will have the opportunity to shape the technical landscape of our AI-powered security solutions, working closely with the CTO and product teams to translate advanced research into scalable, real-world applications. Your primary focus will be on evaluating frameworks, building robust ML pipelines, and integrating AI capabilities seamlessly into our product offerings. Success in this position will be measured by improvements in agent performance, system reliability, and the tangible impact of our AI solutions on customer security outcomes. This role is ideal for a hands-on ML engineer with extensive experience in scaling production systems, a product-oriented mindset, and a passion for solving complex security problems through innovative AI applications.

Reasons to use Rodeo

I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?

Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.

Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.

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Graduate Consultant — 2026 Scheme

PwC·London, UK
£35,000/yr

Why you're a good match

Strong

Your economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.

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Why you're a good match

You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.

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Strong

Experience fit

Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.

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Qualifications

  • 6+ years of experience building and scaling machine learning systems in production environments
  • Strong foundation in classical neural networks and deep learning principles
  • Expertise in modern large language models (LLMs), transformer architectures, and generative AI applications
  • Proficiency in Python and familiarity with various ML frameworks and tools
  • Experience in deploying ML models from experimentation to customer-facing production systems
  • Demonstrated ability to build evaluation frameworks and scalable ML pipelines
  • Experience working across multiple organizations, including startups and scale-ups
  • Strong understanding of product development cycles and integrating ML solutions into user-facing features
  • Excellent collaboration skills with product teams and stakeholders
  • Self-motivated with the ability to operate autonomously while aligning with leadership goals

Responsibilities

  • Design and implement comprehensive evaluation frameworks to measure AI agent performance and track improvements over time
  • Own the entire ML lifecycle, from prototype development to scalable, reliable production deployment
  • Collaborate with product teams to seamlessly integrate ML capabilities into customer-facing features, ensuring technical excellence translates into user value
  • Continuously improve AI agents through experimentation, prompt engineering, and architectural enhancements
  • Build and scale ML infrastructure, monitoring tools, and workflows to support organizational growth and rapid deployment
  • Partner closely with the CTO and engineering leadership to align technical strategies and ensure project success
  • Mentor junior ML engineers through code reviews, technical guidance, and knowledge sharing
  • Stay abreast of the latest advancements in AI, LLMs, and cybersecurity to inform and enhance our solutions

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Benefits

  • Opportunity to work at the forefront of AI and cybersecurity innovation
  • Collaborative environment with experienced leaders from Big Tech and scale-ups
  • Autonomy to lead projects and shape technical strategies
  • Potential for career growth into leadership roles such as Head of ML Engineering or domain technical lead
  • Participation in transformative technology development at the intersection of generative AI and cybersecurity
  • Competitive compensation and benefits package
  • Flexible work arrangements and a focus on work-life balance

Equal Opportunity

Maze is an equal opportunity employer committed to fostering an inclusive environment for all employees. We celebrate diversity and are dedicated to creating a workplace where everyone feels valued, respected, and empowered to contribute their best. We do not discriminate based on race, ethnicity, gender, sexual orientation, age, disability, or any other protected characteristic. All qualified applicants are encouraged to apply.

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Skills

Machine Learning
Large Language Models
Python
Deep Learning
Transformer Architectures
Generative AI
ML Pipelines
Evaluation Frameworks
Cybersecurity
Production Deployment
Prompt Engineering
ML Infrastructure

Location

England, United Kingdom

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