Engineering intelligence
from first principles.
We are building a new class of foundation model that understands the language of engineering—physics, mathematics, and causal reasoning.
What is an SFM?
A Scientific Foundation Model (SFM) is a large-scale model trained to reason with scientific structure—equations, constraints, and empirical data—enabling physically consistent prediction, validation, and design across engineering domains.
Science-Native Learning
We integrate conservation laws, symmetries, and known constraints into training objectives to move beyond text fluency.
From Simulation to Synthesis
SFMs compress iteration cycles: rapid exploration → verifiable designs → deployable solutions, with model-assisted optimization.
Composable by Domain
Adaptable to aerospace, energy, and engineering via modular priors and calibration against domain benchmarks.
Beyond Language: The SFM Core
LLMs master language. SFMs aim to master the laws of science and engineering.
LLM: Pattern Matching
- Trained on text & code corpora
- Statistical correlations
- Describes engineering concepts
- No inherent physical guarantees
SFM: Causal Reasoning
- Trained on simulations & equations
- Encodes constraints (conservation, symmetry)
- Generates verifiable designs
- Targets physics-consistent outputs
How We Build
Rigor over Hype
Testable claims, reproducible pipelines, and honest reporting.
Compound Ownership
Small teams own literature → design → training → eval → integration.
Engineering Excellence
Brings scientists + engineers together.
Velocity with Guardrails
Move fast, measure twice—peer review and safety protocols are default.
Transparent Collaboration
- Open code and experiment artifacts; encourage reproducibility in all projects.
- Frequent cross-team syncs, sharing of insights, and public benchmarks.
- Document decisions (why & trade-offs), not just the outcomes.
Responsible Innovation
- Prioritize data privacy, bias mitigation, and verifiability in model outputs.
- Build for energy efficiency, resource-aware design, and environmental impact.
- Embed safety, fairness, and verification tests early in model and system design.
Open Roles
If a role is an ~80% fit and you learn quickly, we want to hear from you. Demonstrated work beats generic CVs.
Foundation Model Architecture Team
Architect large-scale transformer-based models (50B+ parameters) for India's sovereign AI initiative. Design novel attention mechanisms, tokenization strategies, and position encodings to maximize efficiency.
Data Engineering Team
Build and optimize high-throughput data pipelines for large-scale AI and SciML training. Develop systems for ingesting, cleaning, and curating scientific datasets across multiple domains.
HPC for AI Team
Architect massively parallel systems for training and inference at national supercomputing scale. Optimize performance across multi-node GPU clusters using MPI, NCCL, and custom kernels.
Scientific Computing and SciML Team
Develop physics-informed neural networks (PINNs) and Fourier Neural Operators (FNOs) for complex PDEs. Combine classical numerical methods with modern AI for multi-physics simulation.
Collaborations & Advisors
We co-design benchmarks and share ablations with select research and industry partners.
Follow Our Progress
Request our technical brief or get notified when we release evaluations, publications, or new roles.