🛠 Tech Stack

💼 About This Role

You'll work with research scientists to build and deliver ML models for real-world physics and engineering problems. You'll design, build, and optimize deep learning models and implement distributed training architectures on cloud and on-premise infrastructure. This role offers the chance to own research work-streams and mentor junior colleagues.

🎯 What You'll Do

• Design and optimize ML models for scalability and efficiency
• Transform prototype models into robust, optimized implementations
• Implement distributed training architectures for multi-node/GPU
• Scale foundation model training to large data and cloud compute

📋 Requirements

• MSc or PhD in CS, ML, statistics, physics, engineering, or related field
• 2+ years in a data-driven role with exposure to scaling ML models
• Experience with distributed computing frameworks (Spark, Dask, MPI, CUDA)
• Proficiency in Python and ML libraries (PyTorch, JAX, NumPy)

✨ Nice to Have

• Familiarity with federated learning
• Experience with C/C++ for scientific computing
• Knowledge of MLOps and CI/CD pipelines

🎁 Benefits & Perks

• 🚀 Equity options
• 🏦 10% employer pension contribution
• 🍽️ Free office lunches
• 👶 Enhanced parental leave
• ☀️ 25 days Annual Leave + Public Holidays

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter screen· 30 min
2. 2Technical interview· 60 min
3. 3Team fit interview· 45 min