Principal ML Infra Engineer – Large-Scale Physics Models

Principal ML Infra Engineer – Large-Scale Physics Models

Full-Time 81000 - 99000 Β£ / year (est.) Home office (partial)
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At a Glance

  • Tasks: Scale and operate research infrastructure for large physics models with ML engineers.
  • Company: PhysicsX, a leader in innovative physics research and technology.
  • Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
  • Other info: Join a dynamic team in London with exciting career advancement opportunities.
  • Why this job: Shape the future of physics with cutting-edge ML infrastructure and impactful projects.
  • Qualifications: Experience in ML infrastructure and strong collaboration skills.

The predicted salary is between 81000 - 99000 Β£ per year.

PhysicsX is recruiting a Principal ML Infrastructure Engineer to scale and operate the research infrastructure for training and serving large physics models. You will collaborate with ML engineers and researchers to ensure reliable, high-performance training at scale, while shaping architecture and data pipelines across the stack.

Based in London with a hybrid model, you will influence infrastructure decisions, own platform reliability, and partner with the broader engineering group to deliver.

Principal ML Infra Engineer – Large-Scale Physics Models employer: Linuxconfig

At Sony Interactive Entertainment, we pride ourselves on being an exceptional employer that fosters innovation and collaboration within a dynamic work culture. Our commitment to employee growth is evident through comprehensive training programs and opportunities for advancement, all while working in a vibrant location that encourages creativity and teamwork. Join us to be part of a forward-thinking team that values your contributions and supports your professional journey.

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Contact Details:

Linuxconfig Recruitment Team

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We think you need these skills to ace Principal ML Infra Engineer – Large-Scale Physics Models

Machine Learning Infrastructure
Large-Scale Model Training
Data Pipeline Architecture
Collaboration with ML Engineers
Platform Reliability
High-Performance Computing
Research Infrastructure Management

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