Machine Learning Engineer

A profitable, market-leading global Digital Health technology company delivering AI-driven preventive care and chronic disease management platforms to the US healthcare market.

Operating at hyper-scale with an active user reach exceeding 100 million individuals, the company employs hundreds of professionals worldwide with a core engineering and data hub of nearly 100 specialists in Israel.

The organization uniquely combines proven profitability, long-term commercial stability, and massive data volume with an agile culture and deep social impact.

The office is located in Tel Aviv adjacent to both the train station and the light rail, operating on a hybrid model with two days working from home.

Role Description-

• Serving as a Senior Machine Learning Engineer (MLE), joining a core squad of 5 ML Engineers, Data Scientists, and Analytics Specialists responsible for the algorithmic engine powering real-time production systems.
• Taking comprehensive End-to-End (E2E) ownership of machine learning models: architecting, hardening, deploying, and maintaining models across high-throughput production environments.
• Designing and building scalable ML Infrastructure, automated training/serving pipelines, and feature stores across distributed cloud environments.
• Spearheading robust MLOps practices, automated CI/CD for machine learning, continuous model evaluation, drift detection, and production monitoring.
• Translating complex clinical, sensory, and behavioral data into actionable algorithmic solutions utilizing Supervised/Unsupervised Learning, Gradient Boosting (XGBoost, LightGBM, scikit-learn), Causal Inference, and Reinforcement Learning (RL).
• Collaborating cross-functionally with Software Engineers, Data Engineers, and Product Managers to guarantee ultra-low latency, model reliability, and massive scalability.

Requirements-

• 5+ years of dedicated hands-on experience as a Machine Learning Engineer (MLE) – Mandatory
• Proven, extensive background building and serving ML models directly in real-time Production environments at scale – Mandatory
• Deep, practical experience architecting and deploying systems on Cloud infrastructure (AWS, GCP, or Azure) within SaaS architectures – Mandatory
• Substantial experience operating in high-scale environments, with a strong preference for agile, fast-paced startup backgrounds – Mandatory
• Strong software engineering foundations in Python with expertise in building automated ML pipelines, MLOps tooling, and CI/CD workflows – Mandatory
• Hands-on mastery of classical and modern ML toolkits: scikit-learn, XGBoost, LightGBM, and modern distributed data frameworks – Mandatory
• Solid grasp of model serving, performance optimization, monitoring, and debugging in live operational stacks – Mandatory
• High ownership, proactive problem-solving orientation, and fluent English communication skills – Mandatory

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