ML Data Specialist

Allen Control Systems•Austin, TX

2d•Hybrid

About The Position

Allen Control Systems (ACS) is seeking a Machine Learning (ML) Data Specialist to own the flow of data through our computer vision and machine learning pipelines. You will be the steward of dataset quality and labeling throughput: overseeing the flow of incoming video and label data, reviewing outputs for correctness and quality, and surfacing distribution gaps and procedural issues to the ML Platform team. This will be a hybrid role out of our Austin, TX office.

Requirements

Bachelors Degree in a relevant field and 1+ years of relevant experience in data operations, dataset curation, annotation operations, QA or test engineering, video/imagery analysis, robotics or autonomy data ops, or a comparable role. New graduates with relevant project or internship experience also welcome.
A careful, detail-oriented mindset for reviewing imagery and sensor data, spotting inconsistencies in labels, and reasoning about dataset coverage and edge cases.
Experience running a recurring operational process end-to-end — managing throughput, vendor coordination, QA pipelines, or similar — and driving improvements based on what you observe.
Basic command-line proficiency on Linux (navigating filesystems, running provided scripts, reading logs) and a willingness to grow that fluency on the job.
Clear written communication to surface trends, raise issues, and align with internal engineering teams and external labeling vendors.

Nice To Haves

Familiarity with computer vision concepts (object detection, tracking, segmentation) or prior work at an AV, robotics, drone, or other perception-heavy company.
Basic Python, Bash, or SQL — or strong motivation to learn on the job.

Responsibilities

Own end-to-end data and label flow for ACS ML training and testing, from raw inputs through label review and final ingest into training and testing datasets.
Review labeled data for quality, correctness, and conformance to spec.
Manage data routed to third-party labelers: selecting and prioritizing batches; tracking throughput and labeler quality.
Audit incoming data from internal and third-party sources for coverage and balance; flag procedural issues with collection (e.g., over-collection of certain conditions, gaps in edge cases).
Partner with the ML Platform team to provide insight into dataset composition, distribution gaps, and labeling coverage; help define and track the metrics that matter.
Work in dashboards, spreadsheets, and Linux terminals to inspect data, run pre-built scripts, and maintain operational visibility into the pipeline.
Document procedures and contribute to continuous improvement of data and labeling workflows.