Senior Software Engineer, ML Data

Voxel•San Francisco, CA

1d•Hybrid

About The Position

Voxel is building the future of Computer Vision and Machine Learning for operations, risk, and safety. Our technology addresses the key cost drivers for workers’ compensation, general liability, and property damage. We're hiring a strong software engineer to own the data infrastructure required to train and evaluate our ML models. You’ll work with petabytes of data streaming from thousands of live customer cameras, and build pipelines to ingest, search, label, version and store the highest quality of data required to train state-of-the-art computer vision models. Your work directly shapes how our applied ML team measures and improves model quality. You'll set technical direction, write code, make architecture calls, and partner closely with applied CV, ML infra and Platform engineers.

Requirements

4+ years of experience building and shipping large scale software solutions.
Working knowledge of ML training and evaluation. Understand what makes a good dataset, how to measure model quality, and how data quality affects model performance.
Strong Python. Comfortable across the stack: data mining, data labeling, storage and retrieval.
Track record of owning something end to end: building data products valuable to internal customers.
Bias toward shipping. You'd rather ship something good this week than something perfect next quarter.
Strong communication skills.

Nice To Haves

Experience with implementing data compliance & data governance solutions.
Deep understanding of data for computer vision - object detection, tracking, video understanding.
Familiarity with human in the loop data annotation, auto labeling.

Responsibilities

Drive the roadmap for the data infrastructure that powers Voxel's vision and ML capabilities.
Build petabyte-scale pipelines for ingestion, search, labeling, versioning and storage of camera data.
Develop methods for data mining and automated data collection.
Partner with applied ML engineers on dataset quality for training and evaluation.
Collaborate with the Data Ops team (HITL) to design processes to measure and improve data quality.
Understand the data needs of Vision and AI engineers and design scalable infra solutions that support model improvement and vision capabilities.
Collaborate with the Platform team to store and retrieve data efficiently on the cloud.