Values

The ad says“Experience with streaming, large backfills, data catalogs, metadata systems, access controls, and schema evolution.”
The ad says“Experience with AWS, Kubernetes, Docker, Terraform, workflow systems, Postgres, Redis, object storage, or geospatial and vector indexes.”
The ad says“Experience with streaming, large backfills, data catalogs, metadata systems, access controls, and schema evolution.”
The ad says“Experience with AWS, Kubernetes, Docker, Terraform, workflow systems, Postgres, Redis, object storage, or geospatial and vector indexes.”
The ad says“Experience with geospatial, remote-sensing, scientific, image, or other high-dimensional data.”
The ad says“Experience with geospatial, remote-sensing, scientific, image, or other high-dimensional data.”
The ad says“Experience with AWS, Kubernetes, Docker, Terraform, workflow systems, Postgres, Redis, object storage, or geospatial and vector indexes.”
The ad says“Experience supporting machine-learning training, evaluation, feature, or inference workloads.”
The ad says“Experience supporting machine-learning training, evaluation, feature, or inference workloads.”
The ad says“Experience supporting machine-learning training, evaluation, feature, or inference workloads.”
The ad says“Experience with streaming, large backfills, data catalogs, metadata systems, access controls, and schema evolution.”
The ad says“Experience with AWS, Kubernetes, Docker, Terraform, workflow systems, Postgres, Redis, object storage, or geospatial and vector indexes.”
The ad says“Experience with AWS, Kubernetes, Docker, Terraform, workflow systems, Postgres, Redis, object storage, or geospatial and vector indexes.”
The ad says“Experience with AWS, Kubernetes, Docker, Terraform, workflow systems, Postgres, Redis, object storage, or geospatial and vector indexes.”
The ad says“Experience with geospatial, remote-sensing, scientific, image, or other high-dimensional data.”
The ad says“Experience with streaming, large backfills, data catalogs, metadata systems, access controls, and schema evolution.”
The ad says“Experience with geospatial, remote-sensing, scientific, image, or other high-dimensional data.”
The ad says“Experience with streaming, large backfills, data catalogs, metadata systems, access controls, and schema evolution.”
The ad says“Experience with AWS, Kubernetes, Docker, Terraform, workflow systems, Postgres, Redis, object storage, or geospatial and vector indexes.”
The ad says“Experience with AWS, Kubernetes, Docker, Terraform, workflow systems, Postgres, Redis, object storage, or geospatial and vector indexes.”
The ad says“Experience with AWS, Kubernetes, Docker, Terraform, workflow systems, Postgres, Redis, object storage, or geospatial and vector indexes.”

About Matter Intelligence

Welcome to Matter, where we are building the future of vision AI: pairing a world-first sensor that sees molecular chemistry, temperature, and 3D shape with a Large World Model that will be the most powerful intelligence engine for the physical world. This system doesn't just see what something looks like; it understands everything from a single pixel. We call this Superintelligent Vision.

Our team has delivered technologies to Mars for NASA/JPL, designed advanced sensors for U.S. Defense, and frontier artificial intelligence systems. We are now building the next generation of space- and airborne-based sensing systems.

About the Role

Matter is hiring a Data Infrastructure Engineer to build the systems that transform drone, airborne, orbital, and mission data into durable, versioned datasets for research and products. Reporting to Ignacio Cases Martin, this individual contributor will work across ingestion, processing, storage, lineage, indexing, and scalable access while partnering closely with AI platform, research, product, and reliability teams.

Key Responsibilities

Build reliable ingestion and processing pipelines from sensor and mission systems into curated, versioned, access-controlled datasets.
Design batch and streaming workflows that handle partial delivery, duplicate or missing events, schema drift, backfills, corruption, and recovery.
Choose and operate storage formats, partitioning strategies, catalogs, indexes, and query interfaces appropriate for high-volume scientific and geospatial data.
Maintain lineage across source measurements, transformations, datasets, features, and downstream products so results can be traced and reconstructed.
Build data-quality checks, observability, retention controls, and operational tooling that keep datasets trustworthy as volume and complexity grow.
Partner with Agent Infrastructure, research, Signal and Evaluation, Product Intelligence, and Telemetry teams on stable data contracts and scalable access patterns.

Qualifications

Required

Experience building production data platforms, distributed data pipelines, storage systems, or large-scale backend infrastructure.
Strong software engineering skills in Python and experience with databases, schemas, APIs, workflow orchestration, and automated testing.
Understanding of data-platform failure modes including partial delivery, duplicates, missing events, schema evolution, stale products, corruption, and resource contention.
Experience operating data systems in cloud or containerized environments and diagnosing performance, reliability, and cost issues.
Ability to define clear interfaces and communicate tradeoffs across research, product, infrastructure, and operations teams.

Preferred

Experience with geospatial, remote-sensing, scientific, image, or other high-dimensional data.
Experience with AWS, Kubernetes, Docker, Terraform, workflow systems, Postgres, Redis, object storage, or geospatial and vector indexes.
Experience with streaming, large backfills, data catalogs, metadata systems, access controls, and schema evolution.
Experience supporting machine-learning training, evaluation, feature, or inference workloads.

What Success Looks Like

Sensor and mission data moves into trustworthy datasets through observable, recoverable pipelines.
Researchers and products can access versioned data efficiently without losing provenance or scientific context.
Data contracts, lineage, tests, and operational tooling reduce silent failures and one-off integration work.

Location

This role is based in San Francisco, CA, and requires onsite work.

ITAR Requirements

To comply with U.S. export regulations, applicants must be one of the following:

A U.S. citizen or national
A lawful permanent resident (green card holder)
Eligible to obtain required authorizations from the U.S. Department of State

Employee Offerings and Benefits

At Matter, we believe in rewarding high performance and providing the support you need to thrive. Our compensation and benefits package includes:

Competitive compensation based on experience
Early-stage equity package
100% employer-paid health, dental, and vision coverage
Opportunity to work on novel sensing, data, and AI systems with real-world deployment paths to the largest industries in the world

Matter Intelligence is an equal opportunity employer. We welcome candidates from all backgrounds who can raise the ambition and performance of the team.