Machine Learning Engineer

An innovative, fast-growing Medical Device and MedTech startup engineering a breakthrough multidisciplinary platform combining dedicated hardware sensors, deep learning, and bio-signal analytics.

The company's proprietary technology analyzes physiological sweat-gland biomarkers in real time to detect and predict behavioral and physiological anomalies with high clinical precision.

Targeting primarily the US healthcare market, the company provides an advanced R&D environment tackling complex bio-signal processing and edge-to-cloud AI within a close-knit multidisciplinary team.

Located in the Jerusalem area, operating on a hybrid work model.

Role Description-

• Serving as a Senior Algorithm Engineer / Applied Data Scientist, taking end-to-end technical responsibility for designing, developing, optimizing, and validating state-of-the-art Machine Learning & Deep Learning models.

• Processing and analyzing complex physiological and medical bio-signals to identify trends, extract clinical features, and detect behavioral anomalies.

• Architecting, training, and fine-tuning advanced neural network architectures (e.g., CNNs, RNNs/Transformers) using frameworks such as PyTorch or TensorFlow.

• Driving model optimization, quantization, and GPU-accelerated computing to achieve low latency and maximum efficiency in production/edge environments.

• Actively leveraging next-generation AI-assisted engineering and coding tools (Claude Code, Cursor, GitHub Copilot) to maximize daily productivity across research, prototyping, and code delivery.

• Collaborating cross-functionally within a multidisciplinary environment alongside Hardware engineers, Biomedical specialists, Firmware developers, and System architects.

Requirements-

• B.Sc. or M.Sc. in Computer Science, Electrical Engineering, Biomedical Engineering, Data Science, or a related quantitative discipline – Mandatory

• 5+ years of proven hands-on industry experience as an Algorithm Developer / Data Scientist / ML Engineer – Mandatory

• Deep, hands-on expertise in Machine Learning & Deep Learning using frameworks such as PyTorch or TensorFlow – Mandatory

• Solid theoretical understanding and practical experience with advanced neural network architectures (CNNs, RNNs/LSTMs, Transformers, etc.) – Mandatory

• Active, everyday proficiency with AI coding assistants and development tools (Cursor, Claude Code, GitHub Copilot, etc.) – Mandatory

• Experience with GPU acceleration, CUDA, or model inference optimization techniques (e.g., TensorRT, ONNX) – Mandatory

• Proven background working in Multidisciplinary environments (Hardware + Software) – Significant Advantage

• Experience analyzing Bio-signals, Time-Series Sensor Data, Medical Data, or Physiological Signals – Significant Advantage