TryHackMe is the fastest-growing online cyber security training platform. Our mission is to make learning and teaching cyber security easier by providing gamified security exercises and challenges. Having only been around for a handful of years, we've grown to over 4 million users, and our growth isn't slowing down! 🥷

About the role

We’re looking for a Junior Machine Learning Engineer to join our team in building and deploying production-ready ML systems and working with open-source LLMs with RAG (Retrieval-Augmented Generation) implementation.

This is a great opportunity to gain hands-on experience working on cutting edge projects, working closely with our Senior Machine Learning Engineer, the wider data team and our product squads to build and validate productionised models impacting millions of users. You will also have the chance to contribute to a wide-range of applications, from recommendation engines to advanced AI-powered cybersecurity solutions.

Contribute to the development of recommendation engines tailored to user behaviour, improving product personalisation and engagement.
Implement and enhance LLM RAG applications, focusing on developing innovative solutions that integrate external knowledge with large language models for more accurate and context-aware results.
Work on the “Echo”-AI tutor project, supporting personalised learning experiences with AI-backed tutors, powered by LLM and RAG technology. See more: https://tryhackme.com/echo

Skills & Requirements

1+ years of experience in machine learning concepts, including working with classifiers, LLMs and recommendation systems.
Experience in programming languages commonly used in data science and machine learning, such as Python with experience in software engineering best practices (testing, version control, code review)
Familiarity with LLMs and RAG implementations
Experience with REST APIs and microservices architecture
Understanding of data preprocessing techniques, including cleaning and normalising data.
Basic familiarity with Docker&Kubernetes for setting up and managing ML environments.
Basic SQL knowledge for data extraction and analysis

Bonus Points

Experience with vector databases
Familiarity with fine-tuning open-source LLM models.
Familiarity with libraries like TensorFlow, PyTorch and scikit-learn.

This role is ideal for someone looking to grow their career in ML engineering while working on real-world applications.

Perks & Benefits

🚣 100% Remote - Ideally in and around the UK or +/- 1hr from GMT

🕒 Flexi Time - Choose your own hours as long as you have at least 4 hours of overlap with the UK timezone (from 8am - 6pm)

💻 Tools - a dedicated work laptop + any accessories you need to do your best work.

👕 Swag Pack - start your TryHackMe journey with a sought-after branded swag bundle!

💪 Personal Development - £2,500 training budget to acquire certifications, books and more.

⛱️ Company Retreat - an annual company retreat, fully paid for by us!

🧡 Health Insurance - if you're in a country that doesn't have public health care.

🍼 Enhanced Maternity & Paternity- an enhanced package on top of statutory requirements.

💸 401k / Pension - TryHackMe makes it easy to save money for your retirement

🍕Lunch on us - whether you're a pizza-lover, salad obsessed or a big sushi fan, TryHackMe will cover the cost of your lunch order during our recurring company virtual lunches.

🎟 Free THM subscription for your circle - we know our platform can be transformative, and we want to extend that impact to your family and friends!

Hiring Process:

Initial stage: Screening with the TA team
Stage 1: Intro call with the hiring manager
Stage 2: Take Home Exercise
Stage 3: Final Stage Interview

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Junior Machine Learning Engineer