ML Researcher

Location: Hong Kong / Remote
Type: Full Time

As a ML Researcher at Nex, you will develop new machine learning models and algorithms that push the boundaries of computational perception and interaction on Nex Playground. You will join a small, deeply technical team that combines research and engineering to solve complex problems in sensing, understanding, and multimodal interaction.

The ML Research role emphasizes rapid experimentation, exploring new ideas and methods to expand what our platform can sense, understand, and respond to. You will work within the ML Research group, collaborating closely with ML Engineers who build the infrastructure that accelerates your research.

This role is ideal for researchers who want to see their work directly impact product capabilities while maintaining a focus on cutting-edge innovation.

You are driven by curiosity and technical discovery. You see research as a systematic process of exploration and validation, not just theoretical work. You balance scientific rigor with practical impact, knowing that the best research solves real problems. You thrive in a team that values experimentation velocity and measurable technical improvement.

• Develop novel ML models and algorithms for computational perception and interaction
• Design and execute rapid experiments to validate new ideas and methods
• Explore advancements in computer vision, audio processing, sensor fusion, or related domains
• Collaborate with ML Engineers to integrate research outcomes into training pipelines and production systems
• Measure and track experimentation velocity, the number and quality of validated experiments per quarter
• Contribute to research planning and help define the technical roadmap alongside the Engineering Manager and Tech Lead
• Document research findings and communicate technical progress to the broader team

• 2+ years of hands-on ML research experience in industry, academia, or research labs
• Demonstrable track record of designing and conducting ML experiments from hypothesis to validation
• Proficiency in Python for ML research and experimentation
• Deep expertise with PyTorch or TensorFlow for model development
• Experience training and evaluating ML models on real datasets
• Understanding of model evaluation metrics, experimental design, and statistical validation
• Familiarity with data preprocessing, augmentation, and management for ML workflows
• Experience presenting research findings to technical audiences

• Expertise in real-time inference, model optimization, or efficient architectures
• Experience with self-supervised learning, few-shot learning, or foundation models
• Background in multimodal learning combining vision, audio, and sensor data
• Contributions to open-source ML projects or released research code
• Experience collaborating with engineers to productionize research outcomes
• Familiarity with ML Engineering practices: training pipelines, experiment tracking, MLOps
• Background in edge computing, on-device ML, or resource-constrained environments
• Experience with sensing technologies: cameras, microphones, IMUs, or haptic systems
• Knowledge of privacy-preserving ML, federated learning, or on-device data processing