Senior Machine Learning Engineer – GeoAI Platform

Wherobots builds cloud-native infrastructure for analyzing physical-world data through distributed spatial analytics and raster processing products. The Senior Machine Learning Engineer will architect, build, and operate large-scale geospatial ML infrastructure, developing distributed GPU-aware pipelines for raster data ingestion, feature generation, inference, and publication while ensuring production reliability and reusable platform abstractions.

Responsibilities

• 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
• Raise the bar: Provide technical leadership on architecture, engineering standards, and roadmap, and contribute to architecture and code reviews across the organization