Senior ML Infra Architect for Large-Scale Training

Senior ML Infra Architect for Large-Scale Training

Full-Time 75600 - 92400 Β£ / year (est.) Home office (partial)
Physicsx

At a Glance

  • Tasks: Design and operate infrastructure for cutting-edge ML model training and deployment.
  • Company: PhysicsX, a leader in multi-physics research and innovation.
  • Benefits: Hybrid work, equity, pension, private medical cover, and generous leave.
  • Other info: Dynamic work environment with opportunities for professional growth.
  • Why this job: Join a team pushing the boundaries of ML technology and make a real impact.
  • Qualifications: Experience in ML infrastructure and a passion for optimising complex systems.

The predicted salary is between 75600 - 92400 Β£ per year.

Physics X is recruiting a Principal ML Infrastructure Engineer in London to design and operate the infrastructure powering research model training, fine-tuning, and deployment.

You will work with ML engineers and scientists to scale multi-physics models, optimize pipelines, and ensure reproducibility of experiments.

This role offers a hybrid pattern with our Shoreditch office and remote days, equity, pension, private medical cover, and generous leave.

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Senior ML Infra Architect for Large-Scale Training employer: Physicsx

At PhysicsX, we pride ourselves on being an exceptional employer that fosters a culture of innovation and collaboration. Our team thrives in a flat structure where every voice is valued, and we offer meaningful benefits such as equity options, generous parental leave, and a commitment to personal development. Located in Shoreditch, our hybrid work model allows for a sustainable work-life balance while tackling impactful challenges in AI-driven engineering.

Physicsx

Contact Details:

Physicsx Recruitment Team

We think you need these skills to ace Senior ML Infra Architect for Large-Scale Training

Infrastructure Design
Model Training
Fine-Tuning
Deployment
Collaboration with ML Engineers
Scaling Multi-Physics Models
Pipeline Optimization