Technical Staff Member, Data Systems

Superintelligence

Data systems engineer building databases, storage, catalogs, and lineage infrastructure for Physical Superintelligence’s AI-driven physics discovery. Scaling scientific data platforms across storage tiers and clouds.

Posted 8/16/2026full-timeBoston • Massachusetts • 🇺🇸 United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in building and managing data infrastructure, including ownership of transactional databases and analytical query paths. Proficient in scaling systems, ensuring traceability and reproducibility of scientific results, and conducting economic analyses for data storage strategies.

Highest-signal resume keywords

Data Infrastructure DevelopmentTransactional Database ManagementContent-Addressed StorageDistributed Systems ExperienceScientific Data Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data VersioningSchema EvolutionMetadata ManagementCatalog ScalingData Path EconomicsProvenance TrackingLineage SystemsHigh-Volume ServicesCheckpointingData Platform Ownership

Tools & Technologies

Workflow EnginesSchedulersMulti-Cloud Data PlacementFilesystemsAnalytical Query Paths

Industry Keywords

Scientific DataResearch DataData Storage SystemsImmutable ContentRights Enforcement

Tech Stack

Tools & technologies

Cloud

About the role

Key responsibilities & impact

Build the data plane for scientific results at scale, including content addressing, write-time lineage, and rights metadata
Scale catalogs and query paths for billion-object corpora
Own transactional databases, analytical query paths, filesystems, schema evolution, and database-engine selection
Own ingestion and shared access for scientific corpora and public data feeds
Model and manage replicate-versus-fetch economics across storage tiers and clouds
Build versioned, rights-tracked, lineaged training sets, evaluation corpora, and captured traces for AI
Design systems so results remain traceable and reproducible
Take ownership from specification through deployment and on-call

Requirements

What you’ll need

Five or more years building data infrastructure in production at companies known for engineering rigor
Broad data-infrastructure experience across databases, storage systems, filesystems, catalogs, and data platforms
Experience scaling systems and owning platforms rather than only authoring pipelines or administering systems
Understanding of immutable content, versioned names, provenance, rights enforcement, and retention in data paths
Experience diagnosing catalog, namespace, or metadata-service failures before underlying storage failures
Ability to produce replicate-versus-fetch break-even analyses and defend storage-tiering and placement policies in dollar terms
Content-addressed storage, data versioning, or lineage systems in production preferred
GPU-scale checkpointing experience preferred
Distributed-systems experience with schedulers, workflow engines, or high-volume services preferred
Scientific or research-data experience preferred
Experience with table formats, catalogs at scale, and multi-cloud data placement preferred
Ability to work in or relocate to the Boston-based role; remote consideration is case-by-case
Must address United States work-authorization/sponsorship requirements

Benefits

Comp & perks

Meaningful early-stage equity
Benefits
Competitive compensation
Full ownership of work from spec to ship to on-call
AI-native development process with agentic coding tools
Equal opportunity employer valuing diverse perspectives