Senior ML Researcher – Hybrid Cambridge, Edge AI & Security

CamWebDir

Cambridge

Hybrid

GBP 90.000 - 130.000

Vollzeit

Vor 6 Tagen
Sei unter den ersten Bewerbenden

Eine vollständige Bewerbung in einer Minute

Maßgeschneiderter Lebenslauf und Anschreiben – versandbereit.

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Benefits dieser Stelle

23 days holiday + public holidays, 25+
Birthday day off
Private medical insurance
Life insurance
Salary sacrifice pension
Enhanced family leave
Employee Assistance Program
Cycle to work

Zusammenfassung

Darktrace is seeking a Senior Machine Learning Researcher in Cambridge. You will explore and prototype novel ML ideas, test and implement results with software engineers, and contribute to a distinctive cyber defence platform.

The role combines independent research with teamwork and requires deploying models across software stacks and edge devices, optimizing for latency and memory. The position is hybrid with two days in the Cambridge office.

Qualifikationen

  • PhD or MSc in machine learning or related field.
  • Experience with Python ML libraries (PyTorch, TensorFlow, scikit-learn).
  • Deep understanding of large language models and their applications.
  • Be a team player but able to work autonomously.

Aufgaben

  • Explore solutions to diverse problems using ML techniques, incl. LLMs and traditional methods.
  • Collaborate with software engineers to implement research results into production code.
  • Develop and optimise ML models for edge deployment with focus on latency and memory.
  • Contribute to broader software stack with ML components.

Ausbildung

PhD or MSc in ML

Tools

Python
PyTorch
TensorFlow
scikit-learn
Large language models
Transfer learning
Autonomy
Team collaboration
Linux
Git

Jobbeschreibung

Darktrace is seeking a Senior Machine Learning Researcher in Cambridge. You will explore and prototype novel ML ideas, test and implement results with software engineers, and contribute to a distinctive cyber defence platform.

The role combines independent research with teamwork and requires deploying models across software stacks and edge devices, optimizing for latency and memory. The position is hybrid with two days in the Cambridge office.

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