ML Researcher

Location: Hong Kong or Remote

Type: Full Time

The Role

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.

The Mindset

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.

What You’ll Do

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

Must Have

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

Nice To Have

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