# Research Engineer at Constellation Space

- Company: Constellation Space
- What the company does: ConstellationOS is the ML-native operations platform for satellite fleets. Unified telemetry, link forecasts, and policy-bound orchestration. Start with a 30-day shadow pilot. Backed by Y Combinator.
- Company website: https://constellation.space/
- Type: Startups (AI role)
- Level: Mid level
- Location: San Francisco
- Work setup: On-site
- Pay: $180K to $250K base salary per year (USD)
- Posted: 2026-09-18
- Apply by: 2026-11-02
- Apply: https://jobs.ashbyhq.com/constellation/e1918346-ade2-4ac9-98b1-8bb7be974c59
- Page: https://www.1752.vc/careers/jobs/constellation-space-research-engineer/

## About the role

We're looking for a Research Engineer to sit between our data and our models and make the whole loop faster. You will orchestrate and optimize training runs on long-horizon multimodal sequences, build the pipelines that turn a messy, daily-growing corpus into something our models can learn from, and write the research code (libraries, dataloaders, evaluation harnesses) that lets the rest of the team try ideas quickly and trust what they see. You'll work closely with both our engineering and research teams.

## What they're looking for

- 3+ years building ML systems or research infrastructure, including distributed training runs on 100+ GPUs
- Expert-level Python and deep knowledge of PyTorch internals: DDP and FSDP, mixed precision, gradient accumulation, and the profiling tools to tell a slow model from a starved one
- A track record of building or maintaining research libraries others depend on. Contributions to packages like torch_geometric, torchaudio, torcheeg, torch_brain, neuralsets, or comparable internal tooling are exactly what we're looking for
- Experience with data pipelines over large unstructured and multimodal datasets, and familiarity with columnar and streaming formats (Zarr, Parquet, Arrow, Lance, WebDataset, Vortex) and the tradeoffs between random access and sequential throughput
- Hands-on experience with experiment tracking and dataset versioning tooling (ClearML, Weights & Biases, MLflow, or similar)
- Enough research fluency to read a paper, reimplement a component, and tell whether a loss curve is broken

Tags: Research