About the role

What will you do at Granica?

Location: Mountain View, CA (On-site)

Overview

Most of today's AI is built for text, images, and video.

Enterprise data isn't.

At Granica, we're building Large Tabular Models (LTMs)—foundation models that learn natively from structured and relational enterprise data.

Our research, led by Prof. Andrea Montanari (Stanford), explores how generative AI can learn more efficiently from enterprise data through better representations, data selection, augmentation, and compression.

As a Research Engineer, you'll bridge research and production—turning new ideas into scalable machine learning systems that power the next generation of enterprise AI.

This is not an LLM application engineering role. We're looking for engineers who enjoy implementing machine learning algorithms, building ML systems, and working closely with researchers to bring new ideas into production.

What You'll Work On

Build scalable training, evaluation, and inference pipelines for machine learning systems.

Implement and optimize algorithms for structured and tabular data.

Develop benchmarks, datasets, and evaluation frameworks for new research ideas.

Improve training efficiency, memory usage, and inference performance.

Prototype new ML systems and rapidly validate research ideas.

Collaborate closely with Prof. Andrea Montanari and Granica's research team to translate research into production systems.

What We're Looking For

BS, MS, or PhD in Computer Science, Machine Learning, Mathematics, or a related field.
Strong software engineering and machine learning fundamentals.
Experience building production ML systems or ML infrastructure.
Hands-on experience with PyTorch or JAX.
Strong programming skills in Python.
Experience developing evaluation frameworks, ML pipelines, or distributed systems.
Ability to translate research ideas into reliable, production-quality software.
Experience with representation learning, structured or tabular data, probabilistic modeling, distributed training, or ML systems optimization is particularly relevant.
Bonus
Experience working closely with research teams.
Experience optimizing training or inference at scale.
Experience with CUDA, C++, or Rust.
Contributions to open-source ML systems.
Publications or research experience in machine learning.

Compensation

& Benefits

Competitive salary, meaningful equity, and performance bonus for top performers

401(k) with company match, comprehensive health coverage, and unlimited PTO

Daily catered meals in our Mountain View office

Support for research, publication, and conference participation

At Granica, you'll help build the next generation of enterprise AI—from exabyte-scale data infrastructure, Large Tabular Models (LTMs), and stateful AI agents. Together, we're creating the infrastructure that enables enterprises to own their data, own the intelligence built on it, and scale both efficiently.

Which skills does this role require?

PyTorchJAXPythonMachine Learning SystemsDistributed SystemsRepresentation LearningProbabilistic ModelingML InfrastructureC++Tabular Data OptimizationAlgorithm ImplementationEvaluation FrameworksDistributed TrainingSoftware EngineeringGenerative AIMLOpsStructured DataRelational DataInference OptimizationML PipelinesFoundation ModelsLLMsPrototyping

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