DATA LAB TECHNICIAN

Job Description About Quin Quin is redefining smart safety with real-time event detection and data-driven response systems that save lives. We embed our proprietary technology into helmets and wearables through B2B partnerships with global brands. With a strong foothold in motorcycling and a strategic focus on cycling and industrial safety, our technology is already trusted by top names across the U.S. and Europe. We are a fast-moving, purpose-driven team building industry-leading technology that brings intelligence into safety gear. Every product we ship has a tangible impact, and every engineer at Quin has the opportunity to shape technology that protects lives. Why Quin - Foundation of Intelligence: Your data work is the foundation that ML algorithms are built upon. Quality data you create enables breakthrough algorithms. - Learn ML Pipeline: Gain hands-on experience with the complete ML data pipeline, collection, labeling, validation, and organization. Learn how data science teams work. - Hands-On Technical Work: Work with sensors, test rigs, data tools, and real hardware daily. Develop practical skills with technical systems. - Growth Path: This role is a stepping stone to data science, ML engineering, or algorithm development. Learn the foundations while contributing. Mission Support sensor data collection, labeling, and validation that enables high-quality algorithm development and machine learning. About The Role - We are seeking a Data Lab Technician to join our R&D team and support the data infrastructure that powers our machine learning and algorithm development. You will help collect, organize, label, and validate sensor data used to train and test our crash detection algorithms. - You will work with test fixtures, data collection systems, labeling tools, and validation procedures to create high-quality datasets. This role provides hands-on experience with sensor systems, data pipelines, and the full cycle of ML data preparation. - This position is ideal for someone with hardware testing basics who wants to gain experience in data science and ML, or for someone early in their career who wants to contribute to meaningful technical work while learning. - If you're detail-oriented, interested in data and sensors, and want to support breakthrough ML development, join our team. What You'll Do At Quin, this isn't just a job, it's a mission. Here's how you'll make an impact: - Setup Test Rigs: Assemble and configure test fixtures, mounting systems, and sensor rigs for controlled data collection experiments. Ensure sensors are properly positioned, calibrated, and secured. - Execute Data Collection: Run structured data collection protocols using test rigs, drop setups, and motion simulators. Capture sensor data for various motion scenarios, crash types, and edge cases. Follow procedures precisely. - Label and Annotate Data: Review collected sensor data, identify crash events, classify incident types, label key features, and annotate edge cases. Ensure high-quality, accurate labels for ML training. Use labeling software systematically. - Organize Datasets: Maintain organized data repositories with clear naming conventions, proper metadata, and systematic folder structures. Ensure data is properly archived, backed up, and accessible to algorithm teams. - Assist with Validation: Support algorithm validation by running specific test scenarios, collecting validation data, comparing algorithm predictions against ground truth, and documenting discrepancies. - Maintain Lab Equipment: Keep data collection equipment organized, calibrated, and in good working condition. Charge batteries, update firmware, troubleshoot equipment issues, and maintain test fixture inventory. - Document Procedures: Create and maintain clear documentation for data collection procedures, test setups, labeling guidelines, and data organization standards. Help train new team members. - Support Algorithm Team: Respond to data requests from ML and algorithm engineers promptly. Provide datasets with proper documentation. Help explain data collection conditions and potential data quality issues. Basic Qualifications - Hardware testing basics, lab technician experience, or similar hands-on technical background - High school diploma required; Bachelor's degree in a technical field preferred - Strong attention to detail and systematic, methodical approach to work - Comfortable with computers, learning new software tools, and working with data files - Ability to follow detailed procedures accurately and consistently - Good organizational skills and ability to maintain structured datasets - Interest in data, sensors, machine learning, and technology Good to Have - Python familiarity for basic data processing and visualization - Experience with data labeling, annotation tools, or data organization systems - Understanding of sensor systems, IMUs, or accelerometers from coursework or projects - Background in lab work, technical testing, or data collection environments - Interest in pursuing career in data science, ML engineering, or algorithm development - Proficiency with spreadsheets, databases, and data management tools - Experience with version control (Git) or collaborative data platforms Why You'll Love Working Here - Learn ML Foundations: Gain hands-on experience with how ML models are trained. Understand the data pipeline from collection to deployment. - Systematic Work: If you enjoy organized, methodical work with clear procedures and tangible outputs, this role is satisfying. - Direct Impact: The datasets you create enable breakthrough algorithms. Your labeling quality directly affects model accuracy. - Growth Path: Use this role as a springboard to data science or ML engineering. Learn foundations while contributing. - Hands-On Technical: Work with real sensors, test rigs, and data systems. Develop practical technical skills. - Supportive Team: Work closely with algorithm engineers who appreciate quality data work. Learn from experts. Your Impact You ensure our algorithm teams have the high-quality, well-labeled, properly organized data they need to build accurate crash detection models. The datasets you create directly enable the machine learning that saves lives. Every label you create carefully, every data collection you execute precisely, contributes to models that detect crashes and protect riders.