# Senior Machine Learning Engineer – GeoAI Platform at Wherobots

- Company: Wherobots
- What the company does: About Wherobots Wherobots is the AI Context Engine for the Physical World: the missing infrastructure layer for AI that needs to reason about our physical reality. Backed by Felicis.
- Company website: https://wherobots.com/
- Type: Startups (AI role)
- Level: Senior
- Location: Bellevue, WA
- Work setup: On-site
- Pay: $185K to $275K base salary per year (USD)
- Posted: 2026-05-21
- Apply by: 2026-10-08
- Apply: https://ats.rippling.com/wherobots/jobs/d4d59ed8-4cc1-4fcf-ba37-b8273654bdf4
- Page: https://www.1752.vc/careers/jobs/wherobots-senior-machine-learning-engineer-geoai-platform/

## About the role

This is a distributed-systems-first role with meaningful ML infrastructure ownership. You will spend most of your time building high-throughput, GPU-aware data pipelines that turn massive raster archives into features, predictions, and published outputs at global scale. The role sits at the intersection of distributed systems, ML inference, and geospatial data infrastructure.

## What they're looking for

- Design and operate end-to-end ML pipelines : Build pipelines over massive raster archives such as Zarr and COG, from ingestion to feature generation to inference to publication
- Build high-throughput distributed pipelines : Use Ray (Datasets and actors) with careful control over I/O, compute overlap, and backpressure to keep clusters fully utilized
- Optimize GPU inference at scale : Tune PyTorch inference pipelines using batching, CUDA stream overlap, and memory-aware scheduling to maximize throughput per GPU
- Develop spatial data processing patterns : Implement tiling, overlapping windows, and accumulators that match the access patterns of spatial models
- Ensure production reliability : Build in retries, checkpointing, observability, and cost-efficient scaling so long-running global jobs are debuggable and resilient to failure
- Build reusable platform abstractions : Collaborate on abstractions that generalize across datasets, models, and product use cases so new workflows ship quickly

Tags: Engineering