Founding AI Engineer

Novyte Materials is building a frontier AI engine for materials discovery, a system that learns the structure, physics and governing rules of matter, and uses that intelligence to propose entirely new materials for real-world synthesis.

We operate at the intersection of  machine learning, scientific computing and materials physics , where datasets are sparse, search spaces are astronomical, and model decisions directly influence real laboratory experiments.

If you want to work on AI systems that reason about the physical world, not ad ranking, recommendation systems or standard NLP, this is the place.

Location : Mumbai / Bangalore

Role Description

We are hiring a  Founding AI Engineer  to design and build the core intelligence layer of Novyte’s platform. This is an on-site role in Mumbai / Bangalore, working closely with the founder.

You will architect generative, predictive and physics-aware ML models that operate across multimodal scientific data : graphs, crystal structures, text, spectra, simulations and high-dimensional descriptors. Your work will meaningfully shape the technical DNA of the company.

This is a founder-level role with end-to-end ownership. You will :

Design next-gen ML architectures (GNNs, Transformers, Diffusion-style models)

Build physics-aware representation learning systems

Develop active learning and RL-based exploration loops

Create scalable training pipelines and distributed training workflows

Integrate ML models with first-principles simulation and scientific data

Define foundational research practices, abstractions and model standards

Work cross-functionally with computational chemists, platform engineers and research partners

This is not a routine AIML job. You will work in a frontier scientific domain with deep ambiguity, high complexity and massive real-world impact.

Qualifications

Required

Strong experience with PyTorch or JAX

Expertise in graph learning, geometric deep learning, or multimodal ML

Experience with RL, active learning or uncertainty-aware modeling

Strong engineering fundamentals (clean abstractions, reproducibility, systematic debugging)

Experience working with noisy, sparse or non-standard scientific datasets

Ability to read research papers, build prototypes quickly and reason from first principles

Interest in ML for scientific domains, not generic applied ML

Bonus

Experience with diffusion models, flow matching or generative modeling

Knowledge of materials science, physics-informed ML or simulation-integrated ML

Familiarity with HPC, large-scale training or hybrid compute workflows

Research publications or open-source contributions

Prior startup or founding experience

What Novyte Offers

Founding-level ownership and ability to define core IP

A chance to build the intelligence core of a scientific discovery engine

Deep, technical work with clear milestones and real-world validation

A research-driven environment with full autonomy over modeling direction

Direct impact, your models inform real lab experiments within months