Allen Control Systems
Platform Engineer, Data
About this role
Allen Control Systems seeks a Data Platform Engineer to build scalable infrastructure for large-scale image and video pipelines while optimizing datasets for AI model training. You'll combine data engineering expertise with ML knowledge to design intelligent data curation strategies that maximize model performance for autonomous defense systems.
What you'll do
- Design and develop scalable data infrastructure to handle growing volumes of images and videos
- Implement dataset optimization techniques such as coreset selection, embedding-based filtering, and complexity scoring
- Build and maintain data ingestion pipelines with synthetic data generation and versioning
- Partner with ML engineers to ensure data integrity and model performance alignment
- Apply hard example mining, class balancing, and deduplication strategies
- Orchestrate data orchestration workflows for autonomous systems
What they're looking for
- Data infrastructure and pipeline design
- Machine learning principles and model training dynamics
- Large-scale image and video processing
- Python or similar programming languages
- Data curation and dataset optimization
- Cloud platforms and distributed systems
- Data versioning and management
- SQL and data querying
Opens the application — the Jobs AI extension fills it for you. Set up autofill
Opens the official application on the employer’s site. No login required.
Allen Control Systems
Allen Control Systems builds autonomous defense systems and robotics platforms, developing real-time simulations, control software, and integrated hardware solutions. The company is hiring software engineers, systems integrators, field operations specialists, and quality engineers to support the development and deployment of next-generation autonomous defense technology.
View all jobs at Allen Control SystemsLikely interview questions
- Describe your experience designing and maintaining large-scale image and video data pipelines. What tools and frameworks have you used, and how did you handle data versioning and releases?
- Tell us about a time you optimized a dataset for machine learning model training. What specific techniques did you use (e.g., coreset selection, hard example mining, class balancing), and how did you measure the impact on model performance?