Software Engineer, AI Data Platform

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 the hidden inefficiencies in modern data platforms—slashing storage and compute costs, accelerating pipelines, and boosting platform efficiency. The result: 60%+ lower storage costs, up to 60% lower compute spend, 3× faster data processing, and 20% overall efficiency gains.

Massive data should fuel innovation, not drain budgets. We remove the bottlenecks holding AI and analytics back—making data lighter, faster, and smarter so teams can ship breakthroughs, not babysit storage and compute bills.

• World-renowned researchers in compression, information theory, and data systems
• Elite engineers from Google, Pure Storage, Cohesity, and top cloud teams
• Enterprise sellers who turn ROI into seven‑figure wins.

$65M+ raised from NEA, Bain Capital, A
* Capital, and operators behind Okta, Eventbrite, Tesla, and Databricks. Our platform already processes hundreds of petabytes for industry leaders.

We’re building the default data substrate for AI, and a generational company built to endure.

We make data efficient, safe, and ready for scale—think smarter, more foundational infrastructure for the AI era. Our technology integrates directly with modern data stacks like Snowflake, Databricks, and S3-based data lakes, enabling:

• 60%+ reduction in storage costs and up to 60% lower compute spend

Enterprise leaders globally already rely on Granica to cut costs, boost performance, and unlock more value from their existing data platforms.

We’re unlocking the layers beneath platforms like Snowflake and Databricks, making them faster, cheaper, and more AI-native. We combine advanced research with practical productization, powered by a dual‑track strategy:

• Research: Led by Chief Scientist Andrea Montanari (Stanford Professor), we publish 1–2 top‑tier papers per quarter.
• Product: Actively processing 100+ PBs today and targeting Exabyte scale by Q4 2025.

We’ve raised $60M+ from NEA, Bain Capital, A Capital, and operators behind Okta, Eventbrite, Tesla, and Databricks.

To convert entropy into intelligence
, so every builder—human or AI—can make the impossible real.

This is a deep systems role for someone who lives and breathes distributed infrastructure, understands how data moves at scale, and wants to build the next‑generation AI data platform from the ground up.

• Own the ACID backbone. Design and harden transactional layers and metadata services so that petabyte‑scale tables can time‑travel in microseconds and schema evolution becomes a non‑event.
• Turn metadata into rocket fuel. Build compaction, caching, and pruning services that keep millions of file pointers within 50 ms from lookup to plan.
• Squeeze more signal per byte. Optimize data layouts—from column ordering to dictionary and bit‑packing, bloom filters, and zone‑map indexes—to cut scan I/O by 10× on real‑world workloads.
• Ship adaptive indexing with research. Co‑invent machine‑driven indexes that learn access patterns and automatically re‑partition nightly—no more manual “analyze table” ever again.
• Scale the engine, not the babysitting. Write Spark, Flink, or batch pipelines that autoscale across S3, GCS, and ADLS; expose observability hooks; and survive chaos drills without triggering a pager storm.
• Code for longevity. Write clean, test‑soaked Java, Scala, Go, or C++. Document key in variants so future teams can extend the system—instead of rewriting it.
• Measure success in human latency. If analysts see their dashboards refresh in blink‑level time, you’ve won. Publish your breakthrough and mentor the next engineer to raise the bar again.

You’ve built systems where performance, resilience, and clarity of design all matter. You thrive at the intersection of infrastructure…