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- Competitive reward;
- Growth opportunities and career path;
- Flexible work schedule;
- Regular Performance review;
- Vacations and sick-leave days;
- Technical equipment for comfortable work.
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ML Engineer (Movement Detection)
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Remote
Lviv, UkraineWe are looking for a Movement Detection ML Engineer to drive the research and development ...
Lead Generation Specialist
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Lviv, UkraineHello! We are seeking a talented IT Lead Generation Specialist to join our growing team an...
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Lviv, UkraineHello! We are looking for a proactive and ambitious Sales Manager with experience on a rel...
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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.
Key responsibilities
- 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.
Requirements
- 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.
Good to know
- Experience with AWS Batch, Step Functions, S3, and Docker containerization for scalable model training
- On-device/edge inference: CoreML, TFLite, ONNX Runtime Mobile, or similar export/runtime experience
- Model compression: quantization, pruning, distillation
- Wearables or human activity recognition (HAR) experience specifically
- Weights & Biases or comparable experiment-tracking discipline
- Mobile app-side familiarity (Swift/Kotlin)
Hiring process
- HR interview (up to 45 minutes);
- Technical interview (1 hour).
- Final interview (1 hour).
Benefits
- Discounts and vouchers;
- Well-equipped office;
- Long-lasting cooperation;
- Internal referral program;
- Fully covered or paid individual entrepreneurship taxes;
- Gift packs for valuable dates.
Work process
- Team and project onboarding;
- Qualitative project management;
- Flexible and adaptive work approaches;
- Knowledge management and training;
- Quality project releases following SDLC.
