ML Data Specialist at Allen Control SystemsAustin, Texas, United States — full-time

Company Overview

Allen Control Systems (ACS) is a cutting-edge defense startup founded by two former Navy electrical engineers with a proven track record in robotics and software. We are developing an autonomous gun turret using advanced computer vision and control systems to precisely detect, track, and neutralize enemy drones.

Position Overview

Allen Control Systems is seeking a Machine Learning (ML) Data Specialist to own the flow 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.

What You'll Do:

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.

Required Technical Skills:

Background: 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.

Eye for data quality: A careful, detail-oriented mindset for reviewing imagery and sensor data, spotting inconsistencies in labels, and reasoning about dataset coverage and edge cases.

Operational ownership: 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.

Linux comfort: Basic command-line proficiency