ML Data Pipeline Engineer

Há 6 dias

Brazil Prosigliere Tempo inteiro

We're seeking a Data Pipeline Engineer to own and evolve our exercise recognition training data infrastructure. You'll manage the end-to-end pipeline that collects, synchronizes, validates, and prepares IMU sensor and video data for ML model training. This role combines systems engineering, data quality automation, and hands-on problem-solving in a production environment. What You’ll Do Pipeline Operations & Improvement Maintain and enhance our multi-source data collection system: IMU sensors (via mobile app) and synchronized video streams from gym-based cameras. Improve video capture software robustness, particularly handling network interruptions and operational monitoring. Deploy and monitor services in remote Linux environments with appropriate DevOps practices. Data Quality & Validation Evolve our Python-based QC engine that validates data pre- and post-annotation Implement checks for IMU-video time synchronization, sensor health, and measurement consistency Apply digital signal processing techniques to identify sensor failures, connectivity issues, and measurement irregularities. Develop validation logic comparing annotations against sensor data to ensure temporal alignment. Analysis & Troubleshooting Perform ad-hoc analysis on ~1,200+ workout tasks to classify failure modes Identify whether issues stem from pipeline bugs, sensor problems, or annotation errors Prioritize engineering work based on data quality impact and coordinate with annotation team on fixes Tooling and Visualization Maintain and extend our NextJS UI serving annotators, data scientists, and stakeholders Create visualizations (Chart.js) for QC metrics and signal analysis Integrate with LabelStudio annotation interface What You Bring Required Strong Python programming skills, particularly for data processing pipelines Experience with time-series data and digital signal processing Comfortable working in Linux environments and deploying/monitoring remote services Ability to debug complex multi-component systems (sensors, video, networks, sync) Data quality mindset: designing validation rules, tracking metrics, investigating anomalies SQL/database experience for managing pipeline metadata Highly Valued Video processing experience (RTSP streams, encoding, OCR) Working with sensor/IoT data and handling connectivity challenges NextJS or modern web frameworks for data tooling DevOps practices: containerization, monitoring, logging, alerting Experience with annotation pipelines and ML training data workflows Background in biomechanics, sports science, or wearable sensors Tech Stack Languages: Python (primary), JavaScript/TypeScript (NextJS UI) Data: IMU sensor streams, video (RTSP), time-series analysis, DSP Tools: LabelStudio, Chart.js, Linux/bash, OCR libraries Infrastructure: Remote deployment, monitoring systems You'll Thrive Here If You Enjoy detective work: diagnosing why data doesn't match expectations Balance pragmatism with quality: shipping improvements while maintaining reliability Communicate well across technical and non-technical stakeholders Can work autonomously in a small, mission-driven team

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