# Senior Software Engineer – Foundational Data Systems for AI

**Company:** [Granica](https://hotfix.jobs/companies/granica)
**Location:** San Francisco, CA
**Role:** Data Engineering
**Salary:** $190k – $250k/yr
**Experience:** 5+ years
**Skills:** Distributed Systems, Parquet, Orc, Spark, Flink, Java, Rust, Go, C++, Iceberg, Delta Lake, Hudi
**Posted:** 2025-11-07

> Build foundational data systems for AI at scale, including global metadata, adaptive engines, and intelligent data layouts using distributed systems, columnar formats, and languages like Java/Rust/Go/C++.

## Job Description

## What You’ll Build
- **Global Metadata Substrate**: Architect the transactional and metadata substrate that supports time-travel, schema evolution, and atomic consistency across petabyte-scale tabular datasets.
- **Adaptive Engines**: Build systems that reorganize data autonomously, learning from access patterns and workloads to maintain peak efficiency without manual tuning.
- **Intelligent Data Layouts**: Optimize bit-level organization (encoding, compression, layout) to extract maximal signal per byte read.
- **Autonomous Compute Pipelines**: Develop distributed compute systems that scale predictively, adapt to dynamic load, and maintain reliability under failure.
- **Research to Production**: Implement new algorithms in compression, representation, and optimization emerging from ongoing research. Opportunities to publish and open-source are encouraged.
- **Latency as Intelligence**: Design for minimal time between question and insight, enabling models and humans to learn faster from data.

## What You Bring
- Depth in distributed systems: consensus, partitioning, replication, fault tolerance.
- Experience with columnar formats such as Parquet or ORC and low-level encoding strategies.
- Understanding of metadata-driven architectures and adaptive query planning.
- Production experience with Spark, Flink, or custom distributed engines on cloud object storage.
- Proficiency in Java, Rust, Go, or C++ with an emphasis on clarity and quality.
- Curiosity about theory of the mathematics of compression, entropy, and learning efficiency.
- A builder’s mindset: pragmatic, rigorous, and grounded in long-term systems thinking.

## Bonus
- Familiarity with Iceberg, Delta Lake, or Hudi.
- Research or open-source contributions in compression, indexing, or distributed computation.
- Interest in how data representation affects training dynamics and model reasoning efficiency.

## Compensation & Benefits
- Competitive salary, meaningful equity, and substantial bonus for top performers.
- Flexible time off plus comprehensive health coverage for you and your family.
- Support for research, publication, and deep technical exploration.

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