Distinguished Systems Developer - SQL Engine

InterSystems•Boston, MA

1d•$144,000 - $205,000

About The Position

At InterSystems, we’re redefining the future of analytics and intelligent data platforms. Our high-performance, hyper-capacity engine supports a multi-model storage layer—blending structured, semi-structured, and unstructured data—to power a new generation of AI-native and real-time workloads. We believe in technical craftsmanship, deep collaboration, and a culture where ambitious people can build bold systems. The SQL Engine group is the nerve center of our product—where query processing meets distributed intelligence. We consider engineering a science, an art, and a craft, and we cultivate an environment where the most talented minds can thrive, innovate, and make a lasting impact. This is your chance to design the core foundations of a data platform trusted by the world's most mission critical systems and AI research teams.

Requirements

10+ years of experience in systems-level or database internals engineering
Proven expertise in SQL query engines, optimizers, execution runtimes, or data compiler toolchains
Experience with multi-model data systems (SQL + JSON, vector search, graph traversal)
Expert in at least one systems programming languages, or LLVM-based codegen environments
Strong grasp of relational algebra, compiler construction, and data-intensive workloads
Experience using AI-assisted tools (e.g. copilots, summarisation tools, or chatbots) with an ability to critically evaluate and verify AI-generated outputs
Demonstrated ability to learn, evaluate, adapt to, propose and implement AI-augmented workflows in order to continually enhance your own, and the team’s productivity
Passion for designing software that balances performance, correctness, and usability

Nice To Haves

Familiarity with AI infrastructure or ML inference pipelines is a strong plus
PHD degree or published research experience is highly advantageous.

Responsibilities

Build the next-generation query processing stack, optimizing across both traditional analytics and emerging AI-driven workloads.
Query parsing, validation, and transformation
Logical/physical optimization over multi-model data (row, columnar, stream, document, vector)
Plan generation and adaptive execution strategies
Code generation and vectorized execution
Compiler-accelerated storage access patterns for AI workflows
Collaborative integration with our multi-model storage engine
Design data processing abstractions and execution engines that enable AI-native features like fast vector search, graph traversal, and probabilistic joins—all within a familiar SQL interface.
Architect and implement query optimization and execution components that scale across petabytes
Design tooling and frameworks that make AI and analytics convergence seamless
Collaborate with platform and storage teams to leverage multi-model indexing and caching strategies
Contribute to building programmable, intelligent data pipelines that drive downstream ML model performance
Mentor engineers, guide technical debates, and influence long-term system direction
Champion a culture of quality, rigor, and innovation