Allen Control Systems
Platform Engineer, Data
About this role
Allen Control Systems, a defense startup building autonomous drone-detection systems, seeks a Data Platform Engineer to design scalable infrastructure for curating and optimizing large-scale image and video datasets. You'll apply ML knowledge to implement advanced data strategies like coreset selection and synthetic generation, partnering closely with ML engineers to maximize model performance.
What you'll do
- Design and build scalable data infrastructure for organizing and curating growing image and video datasets
- Implement dataset optimization techniques such as hard example mining, class balancing, and embedding-based filtering
- Develop end-to-end computer vision pipelines including ingestion, QA, standardization, and organization
- Create and manage synthetic data generation workflows for training data
- Build data quality tooling to track balance, drift, and annotation errors with feedback loops
- Own dataset versioning, release management, and metadata cataloging systems
What they're looking for
- Data engineering and systems design
- Python scripting and data processing
- AWS data management and processing
- SQL and Linux
- Image and video data engineering
- ML/AI principles and model training dynamics
- Data quality and validation
- Cross-team communication and documentation
Benefits
- Competitive salary
- Equity package
- Health, dental, and vision insurance
- Paid time off
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Allen Control Systems
Allen Control Systems builds autonomous defense systems, including gun turrets and drone-defense technology, using computer vision, control systems, and robotics. The company is hiring across software engineering, mechanical engineering, testing, and operations roles to support product development and scaled production.
View all jobs at Allen Control SystemsLikely interview questions
- Walk us through your experience building data pipelines for computer vision or image/video datasets. What scale have you worked at, and what were the key challenges?
- Describe your experience with data curation techniques like hard example mining, coreset selection, or embedding-based filtering. How have you applied these to improve model performance?