Machine Learning Engineer

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’re a fast-moving, purpose-driven team building industry-leading technology that brings intelligence into safety gear.

About The Role

We’re looking for a hands-on Machine Learning Engineer with 3–5 years of experience to join our growing R&D team. You’ll play a critical role in developing and optimizing models that analyze real-time sensor data to extract meaningful patterns, trends, and insights. Your work will directly impact core product features used in real-world applications.

This role requires strong data intuition, experience with time-series or sensor-based data, and a desire to build robust and scalable ML systems. You’ll collaborate with hardware, software, and domain experts to bring end-to-end solutions to life.

Key Responsibilities

• Analyze and model real-time sensor data to detect events, classify patterns, and predict behaviors.
• Develop and fine-tune ML models for time-series data using supervised and unsupervised techniques.
• Work on feature engineering pipelines that include signal processing, data normalization, and transformation.
• Collaborate with cross-functional teams (e.g., hardware, firmware, mechanical) to understand data sources and context.
• Contribute to the deployment of ML models in real-time systems, ensuring performance and reliability.
• Design experiments, perform exploratory data analysis, and evaluate model accuracy and robustness.
• Communicate insights and findings to both technical and non-technical stakeholders.
  
  

Basic Qualifications

• B.Tech. or M.S. in Computer Science, Data Science, or a related field.
• 3–5 years of experience working on machine learning, data science, or applied AI projects.
• Proficiency in Python and common ML/data libraries (e.g., scikit-learn, Pandas, NumPy, TensorFlow/PyTorch).
• Solid experience working with real-time or time-series data from sensors (IMUs, force sensors, accelerometers, etc.).
• Strong understanding of pattern recognition and statistical modeling techniques.
• Familiarity with digital signal processing techniques such as filtering, FFTs, and feature extraction.
  
  

Preferred Qualifications

• Understanding of kinematics and biomechanical data analysis.
• Prior experience in gait analysis, movement science, or human activity recognition is a strong plus.
• Knowledge of sensor fusion and multi-modal data integration.
• Experience deploying models in production or on embedded systems.
• Familiarity with tools like MATLAB, SciPy, or OpenSim is helpful.