Machine Learning Engineer (Mid/Senior)

TryHackMe; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Annual training budget

Health insurance

Company retreat

Job summary

TryHackMe is seeking a Senior ML Engineer to build and operate ML systems for personalised cybersecurity learning. You will work on production LLM applications, recommendations, and embedding tools from ideation to deployment.

At senior level you will provide technical leadership and mentor engineers. Responsibilities include shipping LLM products with RAG and evaluation, building embeddings and recommendation systems, and managing the ML lifecycle with data pipelines and A/B tests.

Qualifications

• Three to five years of ML engineering experience for mid-level candidates, or five or more years with end-to-end ML leadership for senior candidates.
• Experience shipping LLM products to users, including RAG, prompt and context engineering, and model evaluation.
• Practical experience building recommendation or personalisation systems.
• Understanding of embeddings, including model selection and adaptation.

Responsibilities

• Develop and maintain LLM applications, including retrieval-augmented generation, agent workflows, and evaluation methods.
• Improve an AI tutor to provide guidance informed by learner activity and course context.
• Build recommendation and personalisation systems for content selection, learning paths, and difficulty adjustment.
• Create embedding models and pipelines for semantic search, content similarity, skill mapping, and learner modelling.
• Manage the ML lifecycle, including data pipelines, offline evaluation, A/B tests, deployment, monitoring, and iteration.
• Work with product and engineering partners to set outcome measures and guide technical choices; at senior level, lead designs and mentor engineers.

Skills

ML engineering

RAG

Prompt engineering

LLM apps

Recommendations

Embeddings

A/B testing

Mentoring

Tools

RAG frameworks

LLM tooling

Embedding pipelines

Evaluation frameworks

Job description

Role overview

Build and operate machine learning systems that support personalised cybersecurity learning. The work spans production LLM applications, recommendations, and embedding-based tools, from shaping an initial idea through evaluation and deployment. At senior level, the role also includes technical leadership, mentoring, and setting engineering practices.

Responsibilities

• Develop and maintain LLM applications, including retrieval-augmented generation, agent workflows, and evaluation methods.
• Improve an AI tutor to provide guidance informed by learner activity and course context.
• Build recommendation and personalisation systems for content selection, learning paths, and difficulty adjustment.
• Create embedding models and pipelines for semantic search, content similarity, skill mapping, and learner modelling.
• Manage the ML lifecycle, including data pipelines, offline evaluation, A/B tests, deployment, monitoring, and iteration.
• Work with product and engineering partners to set outcome measures and guide technical choices; at senior level, lead designs and mentor engineers.

Requirements

• Three to five years of ML engineering experience for mid-level candidates, or five or more years with end-to-end ML leadership for senior candidates.
• Experience shipping LLM products to users, including RAG, prompt and context engineering, and model evaluation.
• Practical experience building recommendation or personalisation systems.
• Understanding of embeddings, including model selection and adaptation.

Benefits and work setup

• Remote work from the UK or approved EU regions.
• Annual training budget of £2,500, work equipment budget, and a paid company retreat.
• Health insurance where universal coverage is unavailable, enhanced parental leave, and retirement contribution options.
• Virtual team lunches and workspace accessories are also covered.