About the role
We are looking for a Software Engineer to help build the BlazerOps platform from the ground up. Someone who takes ownership of hard infrastructure problems, writes code that lasts, and cares deeply about the experience of the ML teams who will use what you build.
About Hyades
Hyades is a Spatial AI research lab on a mission to make machines understand the physical world. We are building the foundational intelligence layer for spatial data and machine learning, and the MLOps platform that delivers that intelligence to enterprise geospatial ML teams.
BlazerOps is a platform that handles everything from raw data ingestion to model deployment, with intelligence built in at every stage. You will be working across the full stack: from the Python SDK that data scientists use every day, to the orchestration layer that runs experiments at scale, to the web interfaces that make the platform accessible.
What you will do
Platform and Infrastructure
- Build and maintain the core BlazerOps platform: pipeline orchestration, experiment tracking, and the Living Representation storage layer.
- Design APIs and data models that are clear, consistent, and easy to extend as the platform grows.
- Architect and implement the services that coordinate automated experimentation across distributed compute environments.
- Ensure platform reliability, observability, and performance at the scale our design partners operate.
Developer Experience
- Maintain and extend the Python SDK that connects ML practitioners to the BlazerOps platform.
- Build CLI tooling that makes complex platform operations feel simple and composable.
- Collaborate with the research team to translate experimental capabilities into stable, production-grade platform features.
- Contribute to technical documentation that helps design partners get the most from the platform.
Requirements
- Solid experience with Python and at least one compiled language (Go, Rust, or C++).
- Strong understanding of distributed systems: task queues, state management, and fault tolerance in production environments.
- Experience building and operating services on cloud infrastructure, preferably AWS or GCP.
- Familiarity with ML workflow tooling: experiment tracking, data versioning, or pipeline orchestration.
- Comfortable owning a system end-to-end: from initial design through deployment and on-call support.
Nice to have
- Experience with geospatial data formats: STAC, COG, GeoTIFF, or similar.
- Familiarity with ML compute frameworks: Ray, Dask, or distributed PyTorch training.
- Experience building developer-facing SDKs or CLI tools.
- Background in data engineering or large-scale data processing pipelines.
- Contributions to open-source projects in the ML or geospatial ecosystem.
- Experience integrating with orchestration platforms: Dagster, Prefect, or Airflow.