ML Researcher

Role overview A research-focused position on a small, deeply technical team advancing computational perception and multimodal interaction for a motion-based active play platform used by families. The role emphasizes rapid experimentation, novel model development, and translating research into product capabilities that directly shape what the platform can sense, understand, and respond to.

Responsibilities - Develop novel ML models and algorithms for computational perception and interaction - Design and run rapid experiments to validate new ideas and methods end to end - Explore advances in computer vision, audio processing, sensor fusion, or related domains - Partner with ML Engineers to integrate research outputs into training pipelines and production systems - Track and report on experimentation velocity and the number and quality of validated experiments per quarter - Contribute to research planning and technical roadmap definition alongside engineering leadership - Document findings and communicate technical progress to the broader team

Requirements - 2+ years of hands-on ML research experience in industry, academia, or research labs - Track record of designing and running ML experiments through hypothesis to validation - Proficiency in Python for ML research and experimentation - Deep expertise with PyTorch or TensorFlow for model development - Experience training and evaluating 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, few-shot, or foundation model approaches - Background in multimodal learning combining vision, audio, and sensor data - Open-source ML contributions or released research artifacts - Experience collaborating with engineers to productionize research outcomes - Familiarity with MLOps practices, training pipelines, and experiment tracking - Background in edge computing, on-device ML, or resource-constrained environments - Experience with sensing technologies such as cameras, microphones, IMUs, or haptic systems - Knowledge of privacy-preserving ML or federated learning

Benefits and work setup - Competitive compensation package - Flexible working hours and vacation policy - Product-driven culture that emphasizes talent and individual growth - Hands-on experience with cutting-edge technologies in the active gaming space