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

Competitive compensation package.
Flexible working hours and vacation policy.
Product-driven culture that treasures talents and individual growth.
Front-row seat and hands-on experience with cutting edge technologies in the evolving gaming field

ML Researcher

Key skills