Senior I Machine Learning Engineer

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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