Established in 1997, Softeq was built from the ground up to specialize in new product development and R&D, tackling the most difficult problems in the tech sphere. Now we've expanded to offer early-stage innovation and ideation plus digital transformation business consulting. Our superpower is to deliver all of this under one roof on a global scale. So let's get started and build a better future together!
We are looking for a hands-on Senior Machine Learning Engineer to spearhead the development of an on-device AI solution for sports analytics. You will architect, train, and deploy lightweight, high-performance models that process dual-leg sensor data (IMU) to recognize complex movement patterns in real-time. This is a pure engineering role requiring deep expertise in time-series analysis and edge optimization.
Key skills and requirements:
1. ML Architectures & Time Series
- Deep Learning for Sequences, deep understanding of modern architectures for time-series processing, specifically:
- TCN (Temporal Convolutional Networks): Dilated 1D Convolutions, Residual blocks, Causal padding;
- RNN Variants: Bi-directional LSTM / GRU, layer stacking;
- Hybrid / Attention Models: 1D-CNN + Attention mechanisms (Transformer-lite), Projection heads;
- Classical ML Baselines: Experience with Random Forest and XGBoost based on strong feature engineering (windowed stats, spectral energy);
- Metric Design: Ability to design robust evaluation metrics (Macro-F1, Confusion Matrix analysis) and handle severe Class Imbalance in real-world datasets.
2. Model Optimization & Edge Deployment
- Optimization Techniques. Hands-on experience compressing models for mobile:
Quantization: Post-training quantization (PTQ) to INT8; - Pruning: Structured pruning of convolutional and recurrent layers;
- Knowledge Distillation: Training lightweight "student" models based on heavy "teacher" models;
- Deployment Stack:
Interoperability: Expert-level knowledge of the ONNX ecosystem (export, validation, versioning, opset compatibility); - Mobile Runtimes: Experience preparing models for Core ML (iOS), TFLite / NNAPI (Android), and ONNX Runtime;
- Constraint Management: Proven ability to optimize models for strict hardware constraints: Inference < 50–80ms, Model Size < 5–10MB.
3. Signal Processing & Data Handling
- Sensor Data (IMU): extensive experience working with raw accelerometer and gyroscope data (6-axis / 9-axis) and understanding motion physics;
- DSP Techniques:
Sensor Calibration & Gravity removal; - Resampling & Synchronization (NTP time sync alignment);
- Normalization techniques (Min-Max, Z-score per session);
- Feature Extraction: RMS energy, Jerk, Spectral Centroid;
- Data Augmentation (Time-Domain): Implementation of Time-warping, Jittering (Gaussian noise), Random window shifts, and Channel dropout.
4. Engineering & MLOps
- Core Stack: Production-quality Python, expert proficiency in PyTorch or TensorFlow;
- Infrastructure: Experience managing cloud training environments (AWS/GCP), GPU resources, and Docker for reproducible training;
- Validation Strategy: Implementation of strict Subject-exclusive validation schemes (preventing specific user data leakage into test sets);
- Data Pipelines: Building pipelines for multimodal data synchronization (Video + Sensor timestamps) and automated window slicing;
- Tooling: Proficiency with experiment tracking tools (e.g., MLflow, Weights & Biases) to benchmark multiple architecture iterations.
5. Soft / Lead Skills (Technical Context)
- Decision Making: Ability to justify architectural choices (e.g., LSTM vs. TCN) through the lens of the "Accuracy vs. Latency" trade-off;
- Cross-Team Integration: Ability to bridge the gap between Data Science and Mobile Engineering, ensuring Python preprocessing logic is correctly replicated in Swift/Kotlin/C++ on the device;
- Documentation: Skills in writing technical specifications (Recording protocols, Model cards, API contracts).
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