Senior Machine Learning Engineer (Sports Tech Edge AI)
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Job Location:
Monthly Salary:
Posted on:
5 hours ago
Vacancies:
1 Vacancy
Job Summary
About Softeq:
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 weve 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.
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.
Location: Poland/EU countries
Type of contract: B2B (Fully Remote)
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 < 5080ms Model Size < 510MB.
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).
Key Skills
- Industrial Maintenance
- Machining
- Mechanical Knowledge
- CNC
- Precision Measuring Instruments
- Schematics
- Maintenance
- Hydraulics
- Plastics Injection Molding
- Programmable Logic Controllers
- Manufacturing
- Troubleshooting
About Company
Softeq started in 1997, and now we have offices in Houston, Vilnius, Munich, and Mexico. As a full-stack company, we help clients create turnkey smart gadgets and standalone components of IoT systems. Come join us!
