Jobs at Granica

Build and scale backend APIs, distributed data pipelines, and data platform infrastructure. The role involves lakehouse and warehouse technologies, workflow orchestration, big data frameworks, infrastructure as code, and reliability monitoring. The engineer will design scalable, reliable, cost-efficient systems, collaborate with customers on integrations, and establish engineering best practices for enterprise AI data infrastructure.

Build and optimize distributed compute infrastructure for large-scale analytical and AI workloads. Responsibilities include improving query execution, scheduling, resource allocation, reliability, workload routing, and compute efficiency across Spark and related systems. The role involves debugging performance bottlenecks, optimizing joins, scans, shuffles, caching, partitioning, and memory usage, and working with lakehouse formats and cloud object storage. Candidates will implement workload optimization algorithms and may contribute to open source or research.

Build foundational lakehouse infrastructure for exabyte-scale AI data environments. Responsibilities include metadata and transaction systems, table maintenance, schema and partition evolution, snapshot isolation, compaction, clustering, file-layout optimization, object-store performance, columnar-format optimization, and query performance across major lakehouse engines. The role also involves debugging distributed systems, implementing compression and data-efficiency algorithms, and contributing to open-source or research efforts.

Lead finance strategy including capital raising, forecasting, performance modeling, and risk management to support company’s growth and IPO readiness.

Develop and prototype novel diffusion and generative learning algorithms for Large Tabular Models. Design efficient training methods, representation learning techniques, benchmarks, and collaborate with academic and engineering teams to translate research into production.

Conduct research and develop algorithms for structured AI models, focusing on efficient data representation, learning, and optimization for enterprise applications. Collaborate with teams to transform theoretical models into practical systems.

As a Research Product Manager, you will oversee complex research programs, turning technical ideas into execution plans and aligning research with production systems to enhance AI capabilities.

The Enterprise Account Executive will manage the complete sales cycle, build relationships with senior leaders, and drive platform adoption within data-intensive organizations.

Design and implement algorithms for data optimization at scale, creating efficient systems for data processing and cost reduction.

Lead technical engagements with enterprise data and AI teams: perform discovery, design and run proofs of value, deploy and configure Granica, analyze platform performance and costs, troubleshoot integrations, present technical and business value, and translate customer feedback into product priorities.