Senior Tech Staff: Safety for Agents & LLM Security

Senior Tech Staff: Safety for Agents & LLM Security

Full-Time 60000 - 80000 Β£ / year (est.) No working from home possible
Cohere

At a Glance

  • Tasks: Advance safety for agents and improve next-gen LLMs through innovative data generation and algorithms.
  • Company: Cohere, a forward-thinking tech company based in Edinburgh.
  • Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
  • Other info: Collaborative environment with cross-functional teams and exciting career development prospects.
  • Why this job: Join a mission to create safer, more trustworthy AI models that make a difference.
  • Qualifications: Strong software engineering skills and a passion for tackling scientific challenges.

The predicted salary is between 60000 - 80000 Β£ per year.

Cohere is seeking a Member of Technical Staff in Edinburgh to advance safety for agents and improve next-generation LLMs.

You will focus on data generation, post-training algorithms, and evaluation methods to ensure safer, more trustworthy models.

You will work with cross-functional ML teams and data annotation, product, and policy groups.

This role requires curiosity for new scientific problems, strong software engineering, and ability to work with messy data.

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Senior Tech Staff: Safety for Agents & LLM Security employer: Cohere

Cohere is an exceptional employer that champions innovation and collaboration, particularly in the dynamic field of AI deployment. With a flexible remote work environment, generous vacation policies, and robust training stipends, we prioritise employee well-being and professional growth. Join us to be part of a forward-thinking team that is making significant impacts in sectors like finance and healthcare.

Cohere

Contact Details:

Cohere Recruitment Team

We think you need these skills to ace Senior Tech Staff: Safety for Agents & LLM Security

Data Generation
Post-Training Algorithms
Evaluation Methods
Software Engineering
Cross-Functional Collaboration
Curiosity for Scientific Problems
Data Annotation