🛠 Tech Stack

💼 About This Role

You'll work with a high-caliber team to translate physics and engineering challenges into ML problem formulations. Your models will predict physical systems using deep learning and probabilistic methods, enabling innovation across aerospace, energy, and automotive sectors. This role offers the chance to own research work-streams and publish at top venues.

🎯 What You'll Do

• Formulate physics challenges as mathematical problems for ML
• Build predictive models using deep learning techniques
• Own research work-streams and communicate results
• Collaborate to translate models into production code

📋 Requirements

• PhD in computer science, ML, or related field
• 2+ years industry experience in data-driven roles
• Experience building deep learning models in Python (PyTorch/JAX)
• Publication record in reputable ML venues (NeurIPS, ICML, etc.)

✨ Nice to Have

• Expertise in operator learning or geometric deep learning
• Experience with generative models for geometry/spatiotemporal data
• Background in physics or engineering

🎁 Benefits & Perks

• 🚀 Equity options - share in the company's success
• 🏦 10% employer pension contribution
• ☀️ 25 days annual leave + public holidays
• 🏥 Private medical insurance - 100% employee cover
• 👶 Enhanced parental leave - 3 months full pay paternity, 6 months maternity

📨 Hiring Process

Estimated timeline: 3-5 weeks · AI estimate

1. 1Recruiter Screen· 30 min
2. 2Technical Interview· 60 min
3. 3Research Presentation· 45 min
4. 4Team Fit Interview· 30 min