Specialist Machine Learning Researcher

Darktrace
Cambridge
19 hours ago
Create job alert

Specialist Machine Learning Researcher – Darktrace


Darktrace is a global leader in AI for cybersecurity, protecting nearly 10,000 organisations with its proprietary AI platform.


Role Overview

As a Specialist Machine Learning Researcher, you will drive innovation across projects, from rapid prototyping to large-scale research, working closely with software engineers to test and implement research outcomes and contribute to our cyber defence methodology.


This hybrid role requires compulsory attendance of 2 days per week at our Cambridge office.


What will I be doing?

You will design and implement cutting-edge solutions to complex problems across multiple domains, leveraging techniques such as large language models (LLMs), statistical methods, and classical machine learning. You will work independently and collaboratively, integrate ML models into the broader software stack, and deliver optimized solutions for edge devices, balancing latency, memory efficiency, and performance.



  • Design robust evaluation frameworks to measure model performance and reliability across diverse use cases.
  • Stay up to date with emerging AI/ML trends and recommend improvements to existing systems.
  • Collaborate with engineering teams to ensure scalability, security, and maintainability of deployed solutions.

What experience do I need?

  • PhD or master’s in machine learning or a related discipline, or equivalent practical experience.
  • Strong proficiency in Python machine learning libraries (PyTorch, TensorFlow, scikit-learn) and deep understanding of LLMs, including transfer learning, embeddings, generative usage, and agentic functionality.
  • Ability to work autonomously and make independent decisions while being a collaborative team player.
  • Familiarity with agentic system tooling (e.g., LangGraph, LangChain, smolagents) and supporting infrastructure (MCP servers, vector databases, memory, ontologies).
  • Experience with cloud AI services (AWS Bedrock, Azure AI Foundry, Vertex AI, Copilot Studio) and diverse ML techniques.
  • Working knowledge of Linux, Git, and basic cybersecurity concepts, including AI-specific threats.

Benefits

  • 23 days’ holiday + all public holidays, rising to 25 days after 2 years of service.
  • Additional day off for your birthday.
  • Private medical insurance covering you, your partner, and children.
  • Life insurance of 4× base salary.
  • Salary sacrifice pension scheme.
  • Enhanced family leave.
  • Confidential Employee Assistance Program.
  • Cycle to work scheme.


#J-18808-Ljbffr

Related Jobs

View all jobs

Specialist Machine Learning Researcher

Machine learning specialist computer

Recruitment Team Manager – Artificial Intelligence (UK Market Focus) Manchester (Hybrid)

Experienced Recruitment Consultant – Artificial Intelligence

Recruitment Team Manager – Artificial Intelligence (US Market Focus) Manchester (Hybrid)

Artificial Intelligence Engineer

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

AI Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation. This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think). If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.

How to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.

Maths for AI Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.