ML Researcher

Nex is on a mission to help families rediscover the joy of movement. Created by parents for parents, Nex combines technology and play to deliver fun, social, and interactive experiences powered by natural body motion, encouraging kids and adults to move more, play more, and have fun together. Nex Playground, the company’s award-winning active play system, is purpose-built to get families moving year-round, with safety and privacy as core considerations in its intentional design. It is certified kidSAFE+ COPPA compliant and built to support healthy, active play for all ages and abilities.

Nex Playground features a growing library of 50+ experiences, including motion and dance games, fitness and educational experiences, and Nex Originals. Content includes collaborations with partners like Hasbro, Sesame Workshop, and NBCUniversal. Nex has been recognized by Fast Company’s Most Innovative Companies, TIME’s Best Inventions, and Parents’ Best Entertainment System for Families, and has earned Red Dot, IDEA, and Core77 international design awards. We encourage you to explore Have FunandIs Motion Gaming Back?, as they offer a deeper look into our culture, values, and explain how our approach to motion gaming differs from previous generations.

Location: Hong Kong / RemoteType: 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 experiencein industry, academia, or research labs
• Demonstrable track record ofdesigning and conducting ML experimentsfrom hypothesis to validation
• Proficiency in Pythonfor ML research and experimentation
• Deep expertise withPyTorch or TensorFlowfor model development
• Experiencetraining and evaluating ML modelson real datasets
• Understanding ofmodel evaluation metrics, experimental design, and statistical validation
• Familiarity withdata preprocessing, augmentation, and managementfor ML workflows
• Experiencepresenting research findingsto technical audiences

• Expertise inreal-time inference, model optimization, or efficient architectures
• Experience withself-supervised learning, few-shot learning, or foundation models
• Background inmultimodal learningcombining vision, audio, and sensor data
• Contributions toopen-source ML projectsorreleased research code
• Experience collaborating withengineers to productionize researchoutcomes
• Familiarity withML Engineering practices: training pipelines, experiment tracking, MLOps
• Background inedge computing, on-device ML, or resource-constrained environments
• Experience withsensing technologies: cameras, microphones, IMUs, or haptic systems
• Knowledge ofprivacy-preserving ML, federated learning, or on-device data processing