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This position was posted on July 10, 2026 and is likely no longer accepting applications. We've kept it here for historical reference. Check out the similar jobs below!
Job Overview
Location
Bay Area Office
Job Type
Full-time
Category
Machine Learning Engineer
Date Posted
July 10, 2026
Full Job Description
📋 Description
- • 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.
- • 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.
🎯 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.
🏖️ 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
Skills & Technologies
See exactly how your profile matches this role — strengths, skill gaps, and what to do about them.
About Granica Inc.
Granica builds an AI efficiency platform that compresses and secures petabyte-scale training data for cloud object stores. Its byte-granular deduplication and privacy filtering shrink S3 and GCS footprints, cutting storage and transfer costs while boosting downstream model accuracy. Designed for data scientists and MLOps teams, the service deploys as a transparent sidecar proxy, enforcing differential privacy and access policies without code changes. Founded in 2022 and headquartered in Palo Alto, the company targets enterprises running computer-vision and NLP workloads that need cheaper, safer data pipelines.
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