Generative ML Staff Engineer - Hybrid Customer-Facing

Generative ML Staff Engineer - Hybrid Customer-Facing

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

At a Glance

  • Tasks: Bridge AI models with customer needs and optimise generative models for real-world applications.
  • Company: Enigma, a leader in generative machine learning with a focus on innovation.
  • Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
  • Other info: Collaborative environment with a focus on real-world scientific and industrial applications.
  • Why this job: Join a dynamic team and make an impact in the exciting field of AI.
  • Qualifications: Deep expertise in generative machine learning and strong integration skills.

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

Enigma is seeking a Member of Technical Staff with deep expertise in generative machine learning to bridge cutting-edge AI models and customer needs. You will join an interdisciplinary team of machine learning researchers, software engineers and domain specialists, deploying and optimising advanced generative models for real-world scientific and industrial applications. This hybrid research and engineering role combines strong theoretical knowledge with practical skills to integrate models into.

Generative ML Staff Engineer - Hybrid Customer-Facing employer: Enigma

Enigma is an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration in the heart of London. With a strong focus on employee growth, we provide ample opportunities for professional development and hands-on experience in cutting-edge technologies within the healthcare sector. Our commitment to reliability, security, and privacy compliance ensures that you will be part of a meaningful mission, making a real impact on clinical monitoring and patient care.

Enigma

Contact Details:

Enigma Recruitment Team

We think you need these skills to ace Generative ML Staff Engineer - Hybrid Customer-Facing

Generative Machine Learning
AI Model Deployment
Optimisation of Models
Interdisciplinary Collaboration
Theoretical Knowledge in Machine Learning
Practical Skills in Software Engineering
Integration of Models