Senior Distributed Systems Engineer

GuruLink; Toronto; On-site; CAD 150,000 - 200,000; Full time

Job summary

A leading AI Research Firm in Toronto seeks skilled engineers to design foundational data systems. Responsibilities include optimizing data processing through innovative metadata solutions and developing intelligent layouts to enhance efficiency. The ideal candidate has a strong foundation in distributed systems and a curiosity for data encoding and compression. Join a high-trust, innovative environment that partners with top-tier research and industry experts to shape the future of enterprise AI infrastructure.

Benefits

Competitive salary

Meaningful equity

Comprehensive health coverage

Flexible time off

Support for research and publication

Qualifications

• Understanding of distributed systems: partitioning, replication, and fault tolerance.
• Experience with columnar formats like Parquet or ORC.
• Familiarity with metadata-driven architectures.

Responsibilities

• Design and implement global metadata substrate.
• Build adaptive engines that optimize data processing.
• Develop intelligent data layouts for efficiency.

Skills

Distributed systems understanding

Columnar formats (Parquet, ORC)

Metadata-driven architectures

Spark or Flink experience

Java, Rust, Go, or C++

Curiosity in compression and representation

Builder's mindset

Job description

Location: Toronto, Ontario

We are an AI research and systems company building the infrastructure for a new kind of intelligence: one that is structured, efficient, and deeply integrated with data.

Our systems operate at exabyte scale, processing petabytes of data each day for some of the world’s most prominent enterprises in finance, technology, and industry. These systems are already making a measurable difference in how global organizations use data to deploy AI safely and efficiently.

We believe that the next generation of enterprise AI will not come from larger models but from more efficient data systems. By advancing the frontier of how data is represented, stored, and transformed, we aim to make large-scale intelligence creation sustainable and adaptive.

Our long‑term vision is Efficient Intelligence: AI that learns using fewer resources, generalizes from less data, and reasons through structure rather than scale. To reach that, we are first building the Foundational Data Systems that make structured AI possible.

The Mission

AI today is limited not only by model design but by the inefficiency of the data that feeds it. At scale, each redundant byte, each poorly organized dataset, and each inefficient data path slows progress and compounds into enormous cost, latency, and energy waste.

Our mission is to remove that inefficiency. We combine new research in information theory, probabilistic modeling, and distributed systems to design self‑optimizing data infrastructure: systems that continuously improve how information is represented and used by AI.

This engineering team partners closely with the Research group, led by Prof. Andrea Montanari (Stanford), bridging advances in information theory and learning efficiency with large‑scale distributed systems. Together, we share a conviction that the next leap in AI will come from breakthroughs in efficient systems, not just larger models.

What You’ll Build

• Global Metadata Substrate. Help design and implement the metadata substrate that supports time‑travel, schema evolution, and atomic consistency across massive tabular datasets.
• Adaptive Engines. Build components that reorganize data autonomously, learning from access patterns and workloads to maintain efficiency with minimal manual tuning.
• Intelligent Data Layouts. Develop and refine bit‑level encodings, compression, and layout strategies to extract maximum signal per byte read.
• Autonomous Compute Pipelines. Contribute to distributed compute systems that scale predictively and adapt to dynamic load.
• Research to Production. Translate new algorithms in compression and representation from research into production‑grade implementations.
• Latency as Intelligence. Design and optimize data paths to minimize time between question and insight, enabling faster learning for both models and humans.

Special Perks

Why Join our team?

• Fundamental Research Meets Enterprise Impact. Work at the intersection of science and engineering, turning foundational research into deployed systems serving enterprise workloads at exabyte scale.
• AI by Design. Build the infrastructure that defines how efficiently the world can create and apply intelligence.
• Real Ownership. Design primitives that will underpin the next decade of AI infrastructure.
• High‑Trust Environment. Deep technical work, minimal bureaucracy, shared mission.
• Enduring Horizon. Backed by NEA, Bain Capital, and various luminaries from tech and business. We are building a generational company for decades, not quarters or a product cycle.

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

Join us to build the foundational data systems that power the future of enterprise AI. Here you will shape the fundamental infrastructure that makes intelligence itself efficient, structured, and enduring.

Must Have Skills

What You Bring:

• Foundational understanding of distributed systems: partitioning, replication, and fault tolerance.
• Experience or curiosity with columnar formats such as Parquet or ORC and low‑level data encoding.
• Familiarity with metadata‑driven architectures or data query planning.
• Exposure to or hands‑on use of Spark, Flink, or similar distributed engines on cloud storage.
• Proficiency in Java, Rust, Go, or C++ and commitment to clean, reliable code.
• Curiosity about how compression, entropy, and representation shape system efficiency and learning.
• A builder’s mindset—eager to learn, improve, and deliver features end‑to‑end with growing autonomy.

Nice to Have Skills

• Familiarity with Iceberg, Delta Lake, or Hudi.
• Contributions to open‑source projects or research in compression, indexing, or distributed systems.
• Interest in how data representation influences AI training dynamics and reasoning efficiency.