Senior ML Researcher: Hybrid, LLMs & Edge AI

Darktrace; Cambridge; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

23 days holiday

Birthday day off

Private medical insurance

Life insurance

Pension scheme

Enhanced family leave

Employee assistance program

Cycle to work

Job summary

Darktrace in Cambridge seeks a Senior Machine Learning Researcher to lead innovative research and prototype efforts. You will collaborate with software engineers to deploy research into the software stack and optimize models for edge environments.

The role emphasizes ML expertise with LLMs, embedding techniques, and fast iteration, while working across security teams in a hybrid setup with two days in the office.

Qualifications

• PhD or master's in ML or related field or equivalent experience.
• Strong Python ML libraries experience (PyTorch, TensorFlow, scikit-learn).
• Deep understanding of large language models and their applications.
• Ability to work autonomously and in teams.
• Familiarity with Linux and Git.
• Experience with cloud AI services (AWS Bedrock, Azure AI Foundry, Vertex AI).
• Experience with a variety of machine learning techniques.
• Knowledge of cybersecurity concepts for AI systems.

Responsibilities

• Explore ML solutions across domains using LLMs, embeddings, and statistical methods.
• Collaborate with software engineers to implement research outcomes in the stack.
• Develop optimised ML models for edge deployments with latency and memory constraints.
• Balance independent work and teamwork while delivering results.
• Contribute to our distinctive cyber defence methodology.

Skills

Python ML libraries

LLMs

Autonomy

Team collaboration

Research expertise

Education

PhD or master's in ML or related field

Tools

Linux

Git

Cloud AI services

Vector databases

Job description

Darktrace in Cambridge seeks a Senior Machine Learning Researcher to lead innovative research and prototype efforts. You will collaborate with software engineers to deploy research into the software stack and optimize models for edge environments.

The role emphasizes ML expertise with LLMs, embedding techniques, and fast iteration, while working across security teams in a hybrid setup with two days in the office.