Founding ML Engineer (Scientific ML/PINNs)

The Role

We are seeking a Founding Machine Learning Engineer to serve as the primary architect of our SciML models. While the InTensors team provides deep domain expertise in the physical laws governing our target ML models, your mission is to engineer the neural architectures that strictly enforce them. 

We need a specialist who can bridge the gap between physical constraints and high-performance, scalable ML model design. At InTensors, we value the advancement of the field and we actively encourage the publication of original research and novel architectures, ensuring you remain a recognized leader at the forefront of the ML community.

Initially, this is a fully remote position, allowing you to contribute from anywhere in the world. As the company grows, it may become necessary to transition to onsite operations to lead our tech teams in person.

Key Responsibilities

• Architectural design: In addition to standard MLPs, you will develop and deploy models with innovative architectures such as neural operators, graph neural networks, or manifold learning architectures, optimized for scientific data.
• Physics integration: Embedding natural laws into neural networks to ensure realistic results.
• Optimization & scaling: Ensure that complex physics-informed models remain computationally efficient, focusing on memory management and training stability for high-dimensional PDE solvers.
• Validation frameworks: Build rigorous testing pipelines to ensure model outputs remain within the physical feasibility bounds defined by our scientific team.

Compensation Structure

This is a role within a pre-revenue startup team. Please read carefully:

• Initial phase: Compensation is fully equity based.
• Post funding phase: Upon a successful fundraising (currently in progress), this role transitions to a base salary + equity package.

We are seeking a candidate who is motivated by the long term vision of the company and the opportunity to lead the scientific direction of a startup.

Requirements

• PhD in computer science, machine learning, or computational physics is highly preferred. We will also consider candidates with a Master’s degree and a strong track record of professional experience in developing SciML models.
• Expertise in physics-informed neural networks (PINNs), DeepONet, or neural operators.
• Ability to design advanced and optimal architectures that extend beyond the standard MLP architecture to build efficient and scalable models for scientific discovery.
• Strong skills in PyTorch, JAX, TensorFlow, Keras, or ONNX.
• Knowledge of CUDA and GPU acceleration for optimizing custom layers and high performance tensor operations.
• A track record of peer-reviewed publications or a documented history of building and scaling complex SciML models.

Type: Founding team (Company registered in UAE)

Application ID: INTSR-CA-2026-G01

Note: Initial compensation is equity only; transitioning to equity + cash upon successful fundraising. Only candidates meeting the educational requirements and the compensation criteria will be considered. Only shortlisted candidates will be contacted for an initial interview.