Описание

Weave Robotics is hiring a Machine Learning Research Engineer, Data in San Francisco to build large-scale data pipelines for robot learning. The role focuses on ingesting, curating, preprocessing, and versioning terabyte-scale fleet data to support model training and evaluation.

Responsibilities

Know the data: characterize coverage, redundancy, and drift: spectral analysis on time-series, distributional statistics, clustering over embeddings.
Find the needles: dig bad data out of terabytes of episodes: bad trajectories, bad annotations, dropped frames, desynced streams, then automate the catch so it never gets through again.
Build the data lifecycle: curation, preprocessing, annotation, augmentation, versioning, and the training-ingest formats and loaders that serve it at full throughput.
Use models as instruments: embedding search to mine scenarios, model loss and disagreement as quality signals, VLM-assisted filtering and labeling.
Improve models through data: partner with researchers on model failures, then build the datasets, processing steps and samplers that target specific capabilities and failure modes, and own the sampling and mixture decisions that go into each run.
Build the eval datasets and benchmarks that measure performance across tasks, environments, embodiments, and model versions.

Requirements

Data engineering at scale: pipelines over terabytes, object stores (S3, GCS), distributed storage, and indexing.
Training ingest: high-throughput formats and dataloaders.
Working ML experience: you can launch a fine-tune, read a loss curve, and design an ablation to test a data hypothesis.
Analytical range: signal processing and statistics on real sensor data, and unsupervised structure-finding (PCA, UMAP, clustering) when the labels don't exist yet.
Data debugging: you can trace a problem from sensor drift through a corrupted episode to a pipeline failure.
Strong Python and software engineering fundamentals.

Nice to have

C++ is a plus.
Batch processing at scale: Ray, Spark, or Dask over terabytes, with cost and throughput judgment.
Workflow orchestration in production: Airflow, Kubeflow, or similar, with retries and monitoring.
Robotics data pitfalls: timestamps, clock domains, and sensor modality quirks.
Robot learning exposure: you

-ve trained policies (VLAs, world models, RL) and can tell a data problem from a model problem.

We offer

Job type: Full-time
Salary: $140K - $200K
Visa: US citizen/visa only
Experience: Any (new grads ok)