Matter Intelligence

Data/ML Infrastructure Engineer

San Francisco · Full-time

Sponsorship not specifiedDetected 117 days ago

PythonDistributed SystemsSQLPostgreSQLRedisAWSDockerKubernetesTerraformMachine LearningA/B TestingDesign SystemsResearch

About the role

This role spans ingestion, processing, storage, compute, and serving, with a strong emphasis on reliability, observability, performance, and cost.
You will work closely with research and product engineering to shorten iteration cycles, improve reproducibility, and raise the quality bar for production systems.
You will define clear interfaces and operational standards that keep the platform trustworthy as data volume, model complexity, and product usage scale.

Design, build, and operate scalable data and ML infrastructure on AWS, including workloads running on Kubernetes
Build and maintain systems for ingestion, processing, storage, and serving, with strong guarantees around data quality, correctness, and operational safety
Partner closely with research to support perception model training and evaluation workflows, enabling faster experimentation and more reproducible iteration
Build platform primitives for observability, data versioning, lineage, evaluation, reproducibility, and operational excellence
Partner with product engineering to ensure data- and model-derived insights are accessible through reliable, low-latency serving and retrieval interfaces
Design systems that enable efficient access patterns for customer-facing products, including search, indexing, and large-scale querying
Meaningful experience building production data infrastructure, ML infrastructure, or distributed systems
Experience building and operating systems on AWS

You have strong software engineering fundamentals and have built production systems where reliability, cost, and performance matter.
You can reason clearly about distributed systems tradeoffs, and you have experience designing data-intensive infrastructure that other engineers depend on.
You are comfortable working across data platform and ML platform concerns, and you understand how tightly coupled they become in production.
You care about reproducibility, debuggability, and developer experience because you have seen how quickly they become bottlenecks.
You work effectively across research and product teams.
Familiarity with modern infrastructure and platform tooling, including Kubernetes, Docker, and Terraform
Experience working with production storage and serving systems such as Postgres and Redis
Familiarity with data and ML workflow tooling such as Metaflow
You are energized by working close to the data, close to the models, and close to the product.

Experience supporting ML training, evaluation, batch inference, or model deployment in production
Familiarity with modern large-scale data patterns and tooling, including streaming, backfills, partitioning strategy, and schema evolution
Exposure to perception, multimodal, or geospatial systems, especially where data originates from real sensors and is used in real products
This is a full-time role based in San Francisco, CA.
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

This listing is sourced directly from Matter Intelligence's careers page and normalized into a canonical job model.