Agilesoft
Research Scientist — Tabular Data Learning
Agilesoft, San Francisco, California, United States, 94199
Overview
Granica is redefining how enterprises prepare and optimize data at the most fundamental layer of the AI stack—where raw information becomes usable intelligence. Our technology operates deep in the data infrastructure layer, making data efficient, secure, and ready for scale. We eliminate hidden inefficiencies in modern data platforms, slashing storage and compute costs, accelerating pipelines, and boosting platform efficiency. Our work results in substantial gains in cost and performance.
What You’ll Do
Invent and prototype new algorithms that advance representation learning and compression for structured and tabular data at petabyte scale
Design cost models and adaptive learners that fuse statistical learning theory with systems-level optimization
Develop novel architectures and approximation schemes that enable efficient inference and training on heterogeneous enterprise data (structured, semi-structured, unstructured)
Create telemetry-driven encodings and embeddings that continuously adapt to real-world data distributions
Partner with Montanari’s research group and Granica’s systems engineers to translate new learning methods into production-grade services used by customers like Lyft, Pinterest, and Snap
Author world-class research papers and design docs, mentor peers, and open-source algorithms where possible
Contribute to the scientific community by publishing results that bridge theory, tabular learning, and scalable infrastructure
What We’re Looking For
PhD in Machine Learning, Statistics, or related mathematical field with focus on representation learning, generalization, or structured data modeling
Publications or applied research in areas such as tabular data learning, feature learning, or multimodal fusion
Strong foundation in optimization, information theory, or statistical learning
Hands-on experience with deep learning frameworks (PyTorch, JAX, TensorFlow) and Python / Rust for high-performance prototyping
Track record of validating ideas against large-scale, real-world datasets or production systems
Pragmatic researcher who seeks elegant, empirically validated approaches to hard data problems
Why Join Granica This is a rare opportunity to build the foundations of learning systems for structured data, working directly with Stanford Professor Andrea Montanari, one of the leading figures in modern statistical learning theory.
Research that ships—you’ll invent theory and implement it in real-world, PB-scale systems
Full-stack math—from cost models and combinatorics to vectorized kernels and scheduling heuristics
Fast feedback loops—prototype overnight, validate on live traces the next day
Extreme ownership—partner directly with core systems engineers and product leads to bring your algorithms to life
High-impact culture—every improvement is measurable in dollars saved, seconds shaved, and human time unblocked
Compensation & Benefits
Competitive salary and equity
Premium healthcare, vision, and dental
Unlimited PTO + quarterly recharge days
Stipends for learning, publishing, or open-source engagement
Equal Opportunity Granica is an equal opportunity employer. We’re committed to building a diverse, inclusive team and encourage candidates from all backgrounds to apply.
Compensation Range: $400K - $800K
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What You’ll Do
Invent and prototype new algorithms that advance representation learning and compression for structured and tabular data at petabyte scale
Design cost models and adaptive learners that fuse statistical learning theory with systems-level optimization
Develop novel architectures and approximation schemes that enable efficient inference and training on heterogeneous enterprise data (structured, semi-structured, unstructured)
Create telemetry-driven encodings and embeddings that continuously adapt to real-world data distributions
Partner with Montanari’s research group and Granica’s systems engineers to translate new learning methods into production-grade services used by customers like Lyft, Pinterest, and Snap
Author world-class research papers and design docs, mentor peers, and open-source algorithms where possible
Contribute to the scientific community by publishing results that bridge theory, tabular learning, and scalable infrastructure
What We’re Looking For
PhD in Machine Learning, Statistics, or related mathematical field with focus on representation learning, generalization, or structured data modeling
Publications or applied research in areas such as tabular data learning, feature learning, or multimodal fusion
Strong foundation in optimization, information theory, or statistical learning
Hands-on experience with deep learning frameworks (PyTorch, JAX, TensorFlow) and Python / Rust for high-performance prototyping
Track record of validating ideas against large-scale, real-world datasets or production systems
Pragmatic researcher who seeks elegant, empirically validated approaches to hard data problems
Why Join Granica This is a rare opportunity to build the foundations of learning systems for structured data, working directly with Stanford Professor Andrea Montanari, one of the leading figures in modern statistical learning theory.
Research that ships—you’ll invent theory and implement it in real-world, PB-scale systems
Full-stack math—from cost models and combinatorics to vectorized kernels and scheduling heuristics
Fast feedback loops—prototype overnight, validate on live traces the next day
Extreme ownership—partner directly with core systems engineers and product leads to bring your algorithms to life
High-impact culture—every improvement is measurable in dollars saved, seconds shaved, and human time unblocked
Compensation & Benefits
Competitive salary and equity
Premium healthcare, vision, and dental
Unlimited PTO + quarterly recharge days
Stipends for learning, publishing, or open-source engagement
Equal Opportunity Granica is an equal opportunity employer. We’re committed to building a diverse, inclusive team and encourage candidates from all backgrounds to apply.
Compensation Range: $400K - $800K
#J-18808-Ljbffr