Applied AI Research Engineer – ML Systems & Structured Data

Applied AI Research Engineer – ML Systems & Structured Data

Location: Bay Area (Mountain View)
Employment Type: Full-time
Work Model: On-site
Department: Research
Compensation: $160K – $240K + Equity

Granica is building the next generation of efficient AI infrastructure
.

Today’s AI systems are limited not only by model design but by the inefficiency of the data that feeds them. At enterprise scale, redundant data, inefficient representations, and poorly optimized learning pipelines create enormous cost and latency.

Granica’s mission is to eliminate that inefficiency.

We combine advances in information theory, machine learning, and distributed systems to design data infrastructure that continuously improves how information is represented and used by AI.

Granica’s research effort is led by Prof. Andrea Montanari (Stanford) and focuses on building learning systems that operate efficiently on large-scale structured and tabular data
.

While much of the industry focuses on text or media models, Granica is building the foundations of AI systems that learn directly from structured enterprise data.

This role focuses on building machine learning systems for structured and tabular data rather than general LLM application development.

The Applied AI Research Team sits at the intersection of theory and production.

Your work will take ideas emerging from fundamental research and turn them into practical algorithms, optimized pipelines, and production-ready ML systems that operate across petabytes of structured enterprise data.

This is a high-ownership role for engineers who can think like researchers and build like systems engineers.

You will translate theory into measurable performance improvements and help define the engineering foundations of structured AI.

• Transform foundational ideas from Granica Research and Prof. Andrea Montanari’s group into scalable algorithms and prototypes
• Build evaluation harnesses, datasets, and benchmarks that measure real signal from research ideas
• Define and improve metrics that quantify progress in structured AI systems

• Develop efficient learning methods for relational, tabular, graph, and enterprise datasets
• Prototype representation learning architectures and compression-aware models
• Explore new approaches for learning from heterogeneous structured data

• Implement fast training and inference pipelines using PyTorch, JAX, or custom kernels
• Optimize memory usage, compute utilization, and data movement
• Improve cost, latency, and throughput for large-scale ML workloads

• Design systems integrating symbolic, relational, and neural components
• Enable AI models to reason over structured datasets without relying on text intermediaries

• Work with Research Scientists to validate hypotheses at scale
• Work with Systems Engineers to integrate algorithms into Granica’s data platform
• Work with Product Engineering to ship features powering real enterprise workloads

• Run controlled experiments and analyze performance improvements
• Deliver results with clear benchmarks and reproducible evaluations
• Drive the cycle from prototype → production → optimization

• Strong background in machine learning, probabilistic modeling, optimization, or large-scale ML systems
• Experience building algorithms for structured, relational, tabular, or graph data
• Ability to reason from first principles about scaling behavior, efficiency, and information flow

• Hands‑on experience with PyTorch, JAX, Tensor Flow, or similar ML frameworks
• Strong programming skills in Python
• Experience with systems languages such as Rust, C++, or CUDA is a plus
• Experience building large‑scale ML pipelines, evaluation frameworks, or distributed systems

• Proven ability to turn research ideas into performant, reliable code
• Comfort working in research-driven environments with ambiguous problem definitions
• Strong experimentation discipline and focus on measurable performance improvements