Staff Software Engineer – Data Infrastructure / Real‑Time Pipelines / Cloud / GPUs (Remote)

Attis is a well-funded Series A climate/AI startup seeking a Staff Software Engineer focused on Data Infrastructure and Real-Time Pipelines. In this deeply hands-on role, you will design, build, and run production systems that serve as the data and compute backbone for an AI Earth System aimed at weather and climate analysis.

Responsibilities:

• Build data ingestion & pipelines
• High-throughput ingest for real-time & batch data (models, satellite, radar, reanalysis, obs)
• Turn messy external feeds into analysis-ready, cloud-optimized datasets
• Design bursty GPU & low-latency workflows
• Architect systems that can spin up large GPU fleets briefly, then scale back down
• Keep end-to-end latency low and costs sane
• Productionize inference and evaluation with research/ML teams
• Own cloud-native systems
• Make pragmatic choices across AWS/GCP, Modal/serverless, containers, batch
• Own monitoring, alerting, SLOs, runbooks, and failure-mode thinking
• Enable the rest of the team
• Build tools/abstractions so researchers and data scientists can run large workflows safely

Requirements:

• Raise the bar on code quality, testing, CI/CD, and observability
• Senior / Staff‑level IC: Typically 7+ years as a hands‑on SWE or data/infra engineer
• You've taken systems from idea → design → production → operations
• Real‑time / large‑scale data: Built and run high‑throughput, low‑latency pipelines or big batch systems
• Designed for reliability, observability, and cost at scale
• Python in production: Strong modern Python for data/infra
• Comfortable turning prototypes into robust, maintainable services
• Cloud‑native (AWS and/or GCP): Deep experience with managed services to build scalable, resilient systems
• Containers + serverless/batch environments (Docker, K8s, Modal or similar)
• Workflow orchestration: Designed and operated complex workflows in Airflow / Dagster / Prefect or similar
• High autonomy: You can ramp quickly on a complex domain
• You're comfortable in a small, distributed, senior team with little hand‑holding
• Domain‑relevant large data: Weather, climate, environmental, satellite, radar, or geospatial data
• Formats like GRIB, NetCDF, Zarr, Parquet, etc
• Earth system / NWP exposure: Worked near numerical weather prediction or large scientific models
• GPU / HPC‑style workloads: Operating GPU‑accelerated or large batch compute (HPC, Slurm, Ray, K8s, Modal)
• Research / open‑science environments: Translating research code into robust production systems (academia, labs, open‑source)
• Early‑stage startup experience: Wearing multiple hats, making pragmatic trade‑offs, and shipping under uncertainty