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. Why It Matters 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. Who We Are 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. Powered by World-Class Investors & Customers $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 Our Mission: We’re building the default data substrate for AI, and a generational company built to endure. What We’re Looking For You’ve built systems where petabyte-scale performance, resilience, and clarity of design all matter . You thrive at the intersection of infrastructure engineering and applied research, and care deeply about both how something works and how well it works at scale. We're looking for someone with experience in: Lakehouse and Transactional Data Systems Proven expertise with formats like Delta Lake or Apache Iceberg, including ACID-compliant table design, schema evolution, and time-travel mechanics. Columnar Storage Optimization Deep knowledge of Parquet, including techniques like column ordering, dictionary encoding, bit-packing, bloom filters, and zone maps to reduce scan I/O and improve query efficiency. Metadata and Indexing Systems Experience building metadata-driven services—compaction, caching, pruning, and adaptive indexing that accelerate query planning and eliminate manual tuning. Distributed Compute at Scale Production-grade Spark/Scala pipeline development across object stores like S3, GCS, and ADLS, with an eye toward autoscaling, resilience, and observability. Programming for Scale and Longevity Strong coding skills in Java, Scala, or Go, with a focus on clean, testable code and a documented mindset that enables future engineers to build on your work, not rewrite it. Resilient Systems and Observability You’ve designed systems that survive chaos drills, avoid pager storms, and surface the right metrics to keep complex infrastructure calm and visible. Latency as a Product Metric You think in terms of human latency—how fast a dashboard feels to the analyst, not just the system. You take pride in chasing down every unnecessary millisecond. Mentorship and Engineering Rigor You publish your breakthroughs, mentor peers, and contribute to a culture of engineering excellence and continuous learning. WHY JOIN GRANICA If you’ve helped build the modern data stack at a large company—Databricks, Snowflake, Confluent, or similar—you already know how critical lakehouse infrastructure is to AI and analytics at scale. At Granica, you’ll take that knowledge and apply it where it matters most…at the most fundamental layer in the data ecosystem. Own the product, not just the feature. At Granica, you won’t be optimizing edge cases or maintaining legacy systems. You’ll architect and build foundational components that define how enterprises manage and optimize data for AI. Move faster, go deeper. No multi-month review cycles or layers of abstraction—just high-agency engineering work where great ideas ship weekly. You’ll work directly with the founding team, engage closely with design partners, and see your impact hit production fast. Work on hard, meaningful problems. From transaction layer design in Delta and Iceberg, to petabyte-scale compaction and schema evolution, to adaptive indexing and cost-aware query planning—this is deep systems engineering at scale. Join a team of expert builders. Our engineers have designed the core internals of cloud-scale data systems, and we maintain a culture of peer-driven learning, hands-on prototyping, and technical storytelling. Core Differentiation: We’re focused on unlocking a deeper layer of AI infrastructure. By optimizing the way data is stored, processed, and retrieved, we make platforms like Snowflake and Databricks faster, more cost-efficient, and more AI-native. Our work sits at the most fundamental layer of the AI stack: where raw data becomes usable intelligence. Be part of something early—without the chaos. Granica has already secured $65M+ from NEA, Bain Capital Ventures, A* Capital, and legendary operators from Okta, Tesla, and Databricks. Grow with the company. You’ll have the chance to grow into a technical leadership role, mentor future hires, and shape both the engineering culture and product direction as we scale. Benefits: Highly competitive compensation with uncapped commissions and meaningful equity Immigration sponsorship and counseling Premium health, dental, and vision coverage Flexible remote work and unlimited PTO Quarterly recharge days and annual team off-sites Budget for learning, development, and conferences Help build the foundational infrastructure for the AI era Granica is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
Granica is 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. Granica’s 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 Granica 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. 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. Bonus 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. Why Granica 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. At Granica, you will shape the fundamental infrastructure that makes intelligence itself efficient, structured, and enduring.
Granica is 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. Granica’s 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 Granica 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. 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. Why Granica 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. At Granica, you will shape the fundamental infrastructure that makes intelligence itself efficient, structured, and enduring.
Granica is 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. Granica’s 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 Granica 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. Define and evolve the global metadata and transactional substrate that powers atomic consistency and schema evolution across exabyte-scale data systems. Adaptive Engines. Architect self-optimizing systems that continuously reorganize and compress data based on access patterns, achieving order-of-magnitude efficiency gains. Intelligent Data Layouts. Pioneer new approaches to encoding and layout that push theoretical limits of signal per byte read. Autonomous Compute Pipelines. Lead development of distributed compute platforms that scale predictively and maintain reliability under extreme load and failure conditions. Research to Production. Collaborate with Granica Research to translate advances in compression and probabilistic modeling into production-grade, industry-defining systems. Latency as Intelligence. Drive system-wide initiatives to minimize latency from insight to decision, enabling faster model learning and data-driven reasoning. What You Bring Mastery of distributed systems: consensus, replication, consistency, and performance at scale. Proven track record of architecting and delivering large-scale data or compute systems with measurable 10× impact. Expertise with columnar formats and low-level data representation techniques. Deep production experience with Spark, Flink, or next-generation compute frameworks. Fluency in Java, Rust, Go, or C++, emphasizing simplicity, performance, and maintainability. Demonstrated leadership—mentoring senior engineers, influencing architecture, and scaling technical excellence. Systems intuition rooted in theory: compression, entropy, and information efficiency. Bonus Familiarity with Iceberg, Delta Lake, or Hudi. Published or open-source contributions in distributed systems, compression, or data representation. Passion for bridging research and production to define the next frontier of efficient AI infrastructure. Why Granica 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. At Granica, you will shape the fundamental infrastructure that makes intelligence itself efficient, structured, and enduring.