ML Data Pipeline Engineer

3 semanas atrás

Recife, 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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