ML Data Pipeline Engineer

Há 3 dias

Manaus, Brasil 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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