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Jose Manuel Rojas

Jose Manuel Rojas

IoT Systems & Data Engineer – Time Series Signal Labeling

CHILE flag
Santiago, Chile
$35.00/hrExpertAws SagemakerInternal Proprietary Tooling

Key Skills

Software

AWS SageMakerAWS SageMaker
Internal/Proprietary Tooling

Top Subject Matter

Industrial IoT sensor data in mining
Clinical research—Parkinson's
voice disorders

Top Data Types

VideoVideo
TextText
DocumentDocument

Top Task Types

Classification
Diagnosis

Freelancer Overview

Electronic Engineer with 3+ years of hands-on experience in multimodal data annotation, time series labeling, speech and audio annotation, and medical imaging annotation across biomedical and industrial domains. Proficient in Python, PyTorch, TensorFlow, and Scikit-learn. Experienced in ground-truth label creation, experimental protocol design, and LLM output evaluation. Annotated datasets from MRI, high-speed video, EEG, and physiological sensors for research-grade ML pipelines. Currently building industrial IoT sensor data pipelines for the mining sector. Fluent in English (IELTS C1) and Spanish (native) — available for AI training projects including data labeling, RLHF preference evaluation, code generation review, and LLM evaluation.

ExpertEnglish

Labeling Experience

IoT Systems & Data Engineer – Time Series Signal Labeling

Computer Code ProgrammingData Collection
As an IoT Systems & Data Engineer, I performed time series annotation and signal labeling from industrial sensor data in the mining sector. My work involved developing sensor data pipelines for real-time monitoring and validating sensor signal quality for ML-ready datasets. I collaborated with operations teams to establish protocols for reliable ground-truth dataset creation. • Labeled vibration, temperature, and pressure signals for predictive analytics. • Validated large-scale sensor streams for industrial equipment health. • Designed data workflows for transforming raw telemetry into annotated formats. • Utilized Python and internal tooling for automated labeling tasks.

As an IoT Systems & Data Engineer, I performed time series annotation and signal labeling from industrial sensor data in the mining sector. My work involved developing sensor data pipelines for real-time monitoring and validating sensor signal quality for ML-ready datasets. I collaborated with operations teams to establish protocols for reliable ground-truth dataset creation. • Labeled vibration, temperature, and pressure signals for predictive analytics. • Validated large-scale sensor streams for industrial equipment health. • Designed data workflows for transforming raw telemetry into annotated formats. • Utilized Python and internal tooling for automated labeling tasks.

2026 - Present

Assistant Research Engineer – Multimodal Medical Data Annotation

Medical DicomDiagnosis
As an Assistant Research Engineer, I conducted multimodal annotation including medical imaging (3T MRI), high-speed video, and physiological signals in clinical research. This work focused on creating expert ground-truth labels for training ML models for the diagnosis of neurological and laryngeal disorders. I designed, validated, and implemented detailed annotation protocols for Parkinson's disease and vocal hyperfunction datasets. • Annotated magnetic resonance images using clinical research standards. • Labeled high-speed endoscopic video and articulatory features in audio. • Applied established clinical rubrics for labeling speech pathology data. • Processed and curated time series from biosignal acquisition platforms.

As an Assistant Research Engineer, I conducted multimodal annotation including medical imaging (3T MRI), high-speed video, and physiological signals in clinical research. This work focused on creating expert ground-truth labels for training ML models for the diagnosis of neurological and laryngeal disorders. I designed, validated, and implemented detailed annotation protocols for Parkinson's disease and vocal hyperfunction datasets. • Annotated magnetic resonance images using clinical research standards. • Labeled high-speed endoscopic video and articulatory features in audio. • Applied established clinical rubrics for labeling speech pathology data. • Processed and curated time series from biosignal acquisition platforms.

2023 - 2025

Principal Research Engineer – EEG and Sensor Data Labeling

Medical DicomClassification
As Principal Research Engineer, I coordinated clinical data collection and annotation for EEG signal studies and implemented quality-controlled labeling workflows. My responsibilities included ensuring inter-annotator reliability and adherence to annotation protocols across a large participant cohort. I contributed to the creation of annotated datasets supporting research on vocal hyperfunction and speech dynamics. • Labeled EEG sensor data for neurological signal analysis. • Developed annotation guidelines and enforced protocol compliance. • Supervised and calibrated biosignal acquisition and video hardware. • Created workflow for wearable accelerometer data in ambulatory monitoring.

As Principal Research Engineer, I coordinated clinical data collection and annotation for EEG signal studies and implemented quality-controlled labeling workflows. My responsibilities included ensuring inter-annotator reliability and adherence to annotation protocols across a large participant cohort. I contributed to the creation of annotated datasets supporting research on vocal hyperfunction and speech dynamics. • Labeled EEG sensor data for neurological signal analysis. • Developed annotation guidelines and enforced protocol compliance. • Supervised and calibrated biosignal acquisition and video hardware. • Created workflow for wearable accelerometer data in ambulatory monitoring.

2021 - 2023

Education

U

Universidad Técnica Federico Santa María

Bachelor of Science, Electronic Engineering

Bachelor of Science
2016 - 2022

Work History

U

Universidad Técnica Federico Santa María

Teaching Assistant – Digital Signal Processing with Applications

Valparaíso
2021 - 2022