Senior Machine Learning Engineer at Softeq

Warsaw, Vilnius County, Lithuania -

Full Time

Start Date

Immediate

Expiry Date

09 Sep, 26

Salary

0.0

Posted On

11 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Time-Series Analysis, Deep Learning, Model Quantization, ONNX, PyTorch, TensorFlow, Signal Processing, Edge Deployment, TCN, LSTM, XGBoost, MLOps, Docker, AWS, GCP, Core ML

Industry

IT Services and IT Consulting

Description

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. Location: * Vilnius, Lithuania (employment or B2B contract, hybrid/remote work) * Warsaw, Poland (B2B contract, 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 < 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). Softeq communicates only from @softeq.com [https://softeq.com/] email addresses. We never request payments or fees for any reason during hiring — including trainings or courses to be completed, equipment, onboarding, or background checks — and we will not ask for cryptocurrency or gift cards. If you receive a message from any other domain or requesting payment, do not respond and report it to abuse@softeq.com [abuse@softeq.com]

Responsibilities

Lead the development of an on-device AI solution for sports analytics using dual-leg sensor data. Architect, train, and deploy high-performance, lightweight models for real-time movement pattern recognition.