ML Engineer (Movement Detection)

We are looking for a Movement Detection ML Engineer to drive the research and development of exercise-recognition models from IMU sensor data (accelerometer/gyroscope) and scale coverage from our current ensemble to several hundred exercises, while taking inference live on-device.

• Increase accuracy and expand coverage to several hundred exercises. You will fine-tune existing models, redesign ensembling strategies, evaluate architectures suited for long-tail, imbalanced multi-class time-series classification, and adapt training regimes (data augmentation, sampling, curriculum learning) for newly annotated data.
• Port the server-side inference pipeline to run live on iOS/Android devices under strict latency, memory, and compute constraints, handling real-world sensor noise and dropped samples through model export, quantization, and distillation.
• Ensure all architectural and ensembling wins on offline accuracy respect mobile edge-budget constraints right from the design phase.
• Hands-on experience around 5 years with PyTorch and PyTorch Lightning for building, training, and extending production pipelines, along with Python proficiency
• Strong applied deep learning background grounded in numerical/signal time-series data (IMU, EMG, audio, sensor fusion, biosignals, or industrial time-series) using architectures such as 1D-CNNs, RNNs/GRUs, dilated convolutions, or Temporal Transformers.
• Comfortable with ensembling and cascade-style model architectures - combining multiple models' outputs and evaluating latency/complexity vs. performance trade-offs.
• Solid grasp of evaluation metrics for imbalanced, multi-class time-series classification/segmentation across hundreds of classes (precision/recall trade-offs, label noise, sensor placement variance).
• Able to work independently with a large, configuration-driven codebase, respecting established architectural conventions and software design patterns.
• Strong Upper-Intermediate (B2+) or higher - comfortable with daily written and spoken technical communication.
• Working hours: 15:00 - 23:00 (Kyiv time) - preferred working hours, but the schedule can be adapted.