Machine Learning Engineer — Healthcare Foundation Models

Job Requirements Role Overview At M42, we are innovating the global healthcare landscape by combining advanced health tech, clinical excellence, and precision genomics across 27 countries. As a Machine Learning Engineer within our Core AI & Digital Functions team, you will be responsible for scaling clinical intelligence from initial training models to production-grade applications. You will build at the crucial intersection of Large Language Models (LLMs), multi-omics data, and deep learning architectures to support our next-generation automated predictive care tools, clinical workflows, and our consumer app ecosystem. This is a highly specialized corporate function tailored directly to moving the needle on our group-wide system AI agenda. Key Responsibilities Multi-Modal Systems Engineering: Build, adapt, and scale production-ready deep learning models utilizing healthcare-centric text data, high-resolution diagnostic imaging, and complex genomic data streams. Agentic Architecture & Orchestration: Design, configure, and maintain advanced Retrieval-Augmented Generation (RAG) structures, policy-driven graph components, and tool calling interfaces. Production Lifecycle Integration: Collaborate closely with distributed cloud engineering squads, product leaders, and healthcare compliance officers to deploy secure, performant models over our core enterprise layers. Distributed Data Pipelines: Construct and scale performant data pipelines optimized for large-scale clinical processing, including multi-lingual healthcare documentation workflows and DICOM imaging layers. Work Experience Essential Requirements Experience Factor: 5+ years of professional software engineering experience, with a dedicated focus of at least 2 years in production-level machine learning engineering or building Generative AI pipelines. Modern Tech Stack: Advanced architectural command of Python, PyTorch, or TensorFlow, alongside deep-tier distributed data query capabilities (SQL, Spark). Interface & REST Architectures: Demonstrated track record shipping secure, production-ready B2B SaaS APIs connecting advanced model configurations to underlying application frameworks. Enterprise Infrastructure Infrastructure: Hands-on experience with modern cloud delivery stacks, infrastructure-as-code, and microservices lifecycle orchestration (Docker, Kubernetes). Education: Bachelor's degree in Computer Science, Data Science, Applied Mathematics, or a highly quantitative technical engineering discipline. Desired Attributes Specialized Domain Context: Prior experience engineering AI features inside clinical informatics, digital health-tech software, or regulated biopharma software systems. Model Optimization: Proven knowledge of model compression frameworks, parameter-efficient fine-tuning (PEFT), and state-of-the-art semantic query mechanics. Academic Background: Graduate-level degrees (Master's or Ph.D.) or research project execution alongside top-tier technical institutions (such as Stanford, Carnegie Mellon, or Georgia Tech). Benefits What We Offer Strategic Ownership: Own a critical pillar of M42's core corporate tech mission, working within the top-performing product development pod in the region. Total Compensation: Highly competitive tax-free base salary package paired with robust enterprise equity allocations. Continuous Upskilling: An annual personal learning and continuous development stipend of $5,000 to invest in frontier models training, specialized workshops, and AI bootcamps. Health & Wellness: Premium comprehensive health, dental, and life protection coverage across our unified healthcare network. Workspace Flexibility: Hybrid operational rhythm with 3 days onsite at our Abu Dhabi digital headquarters and remote options on Mondays and Fridays.