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.
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
More about this role
Constellation is creating the AI-human translation layer that ensures humanity evolves alongside our technology. Our mission is to leverage AI towards addressing deep and meaningful problems at the core of the human experience: empowering people towards their goals, augmenting our cognition and emotional wellness, and understanding ourselves and each other. Our path forward is to move away from AI that captures human knowledge towards AI that truly understands what it is to be human. We are generating the richest multimodal dataset ever collected to build a new class of foundation models and we're seeking the team of researchers that will build them.
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.
Orchestrate and optimize...
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