Senior ML Researcher for Cyber Defense (Hybrid)

Darktrace; Cambridge; Hybrid; GBP 90,000 - 120,000; Full time

Benefits offered by this job

Holidays 23 days

Birthday day off

Private medical

Life insurance

Pension scheme

Enhanced family leave

Employee assistance

Cycle to work

Job summary

Darktrace, a global AI-driven cybersecurity leader, seeks a Senior Machine Learning Researcher in Cambridge. You will explore innovative ideas, prototype research, and collaborate with software engineers to integrate outcomes into our security platform.

The role emphasizes machine learning expertise, including large language models, with responsibilities spanning independent research and team projects, plus deploying models across diverse environments and edge devices for optimized latency and

Qualifications

• PhD or master’s in ML or related field, or equivalent experience.
• Experience with Python ML libraries: PyTorch, TensorFlow, scikit-learn.
• Strong understanding of LLMs and applications (transfer learning, embeddings, generative use).
• Ability to work both independently and in a team; make autonomous decisions.

Responsibilities

• Prototype new ideas and lead research initiatives.
• Collaborate with software engineers to deploy research outcomes.
• Implement ML models in the wider software stack with edge deployment considerations.
• Optimize models for latency and memory across settings.

Skills

PhD/MSc ML

LLMs expertise

Python programming

Autonomous work

Team collaboration

Education

Master's or PhD in ML

Tools

PyTorch

TensorFlow

scikit-learn

Linux

Git

Job description

Darktrace, a global AI-driven cybersecurity leader, seeks a Senior Machine Learning Researcher in Cambridge. You will explore innovative ideas, prototype research, and collaborate with software engineers to integrate outcomes into our security platform.

The role emphasizes machine learning expertise, including large language models, with responsibilities spanning independent research and team projects, plus deploying models across diverse environments and edge devices for optimized latency and