Senior AI Research Engineer | Remote Cybersecurity AI in London

Senior AI Research Engineer | Remote Cybersecurity AI in London

London Full-Time 70000 - 90000 Β£ / year (est.) No working from home possible
Harnham

At a Glance

  • Tasks: Lead the design and deployment of cutting-edge AI systems in cybersecurity.
  • Company: Join Harnham, a leader in AI research with a focus on innovation.
  • Benefits: Enjoy remote work flexibility, competitive salary, and opportunities for professional growth.
  • Other info: Work autonomously in a dynamic environment with excellent career advancement potential.
  • Why this job: Make a real impact in cybersecurity by developing scalable AI models.
  • Qualifications: Experience in machine learning, deep learning, and generative AI required.

The predicted salary is between 70000 - 90000 Β£ per year.

Harnham seeks a Senior AI Research Engineer to lead the design, development and deployment of production AI systems across machine learning, deep learning and generative AI.

You will own end-to-end AI research initiatives with substantial autonomy and work on scalable models from concept to production.

The role requires building and optimizing RF, XGBoost, DL and generative AI, plus RAG, fine-tuning and prompt engineering.

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Senior AI Research Engineer | Remote Cybersecurity AI in London employer: Harnham

Join a leading UK-based media organisation at the forefront of digital transformation, where your role as Director of Streaming Product and Growth will be pivotal in shaping the future of their subscription services. With a strong emphasis on innovation and a collaborative work culture, you'll have access to significant growth opportunities, competitive salary packages, and a performance-based bonus structure, all while working in a dynamic environment that values data-driven decision making and customer-centric strategies.

Harnham

Contact Details:

Harnham Recruitment Team

We think you need these skills to ace Senior AI Research Engineer | Remote Cybersecurity AI in London

Machine Learning
Deep Learning
Generative AI
RF (Random Forest)
XGBoost
Model Optimization
RAG (Retrieval-Augmented Generation)