Scientific ML Engineer (PINN / Operator Learning)
Location: Remote, Africa
Full Time
About the Startup
We are an early-stage, Africa-based, remote-first stealth startup developing physics- and science-informed AI systems to address complex scientific and environmental challenges. At the very start of our journey, we are laying the foundations for tools that combine advanced computational methods, spatial intelligence, and data-driven modeling to generate actionable insights across scientific and environmental domains.
Our approach is mission-driven, focusing on applying rigorous scientific methods and AI/ML to environmental and climate challenges in Africa. We cultivate a collaborative, multidisciplinary culture that values careful experimentation, curiosity, and practical impact.
Our team is remote-first across Africa, allowing talented individuals to contribute from anywhere on the continent while participating in a community committed to innovation, learning, and meaningful scientific impact.
Core principles and benefits of working with us include:
- Mission-driven impact: Contribute to initiatives that apply rigorous science and AI/ML to strengthen environmental and climate understanding across Africa
- Flexible, remote work: Collaborate with an Africa-based team from anywhere on the continent
- Growth Opportunity: Access to professional learning platforms and resources for continuous skill development
- Collaborative culture: Join a multidisciplinary, values-aligned team operating at the intersection of science, technology, and social impact
- Highly competitive compensation and equity options: Receive packages that reflect your expertise and contributions, with the potential to gain equity in the company
- Access to cloud services, latest development tools, and freedom to experiment
Role Overview
You will build, train, and optimize physics-informed ML models for climate and environmental prediction. Work hands-on with PINNs, operator learning, and HPC/GPU workflows to push the boundaries of AI for scientific computing.
Key Responsibilities
- Develop physics-informed ML models and neural operators
- Integrate scientific PDEs, constraints, and domain knowledge into models
- Train large-scale scientific ML models on HPC/GPU clusters
- Collaborate with climate scientists and geospatial engineers
- Optimize models for speed, accuracy, and stability
- Support deployment of scientific models into production pipelines
Requirements
- 3-5 years experience in scientific ML, PINNs, or operator learning
- Experience with PyTorch, JAX, or TensorFlow for custom model development
- Understanding of PDEs, numerical methods, and physical modeling
- Experience training ML models on multi-GPU or distributed systems
- Ability to collaborate in scientific workflows and interdisciplinary teams
- Comfortable communicating in English, both written and verbal
Preferred
- Experience with climate models, environmental physics, or computational science
- Familiarity with differentiable physics, Fourier neural operators, or PDE solvers
- Contributions to scientific ML libraries or research is a plus
Desired Skills
- Artificial Intelligence (AI)
- Geographic Information System (GIS)
- Machine Learning
- PyTorch
- TensorFlow
How to Apply
Find more details here https://www.notion.so/Geospatial-Remote-Sensing-Engineer-2b38d7ddd33580a7b205d59cb0a5528a
