Research Engineer – Machine Learning Systems
About The Position
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.
Requirements
- 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.
Nice To Haves
- 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.
Responsibilities
- 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.
Benefits
- Competitive salary
- meaningful equity
- performance bonus for top performers
- 401(k) with company match
- comprehensive health coverage
- unlimited PTO
- Daily catered meals in our Mountain View office
- Support for research, publication, and conference participation
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What This Job Offers
Job Type
Full-time
Career Level
Senior
