Experienced Machine Learning Engineer; Agentic Systems, Healthcare – Remote

Noble Cortex is seeking a Machine Learning Engineer with a passion for building agentic systems and intelligent healthcare infrastructure. This role offers the opportunity to work on the front lines of AI innovation, designing adaptive, serverless architectures that support real-time decision-making in remote patient monitoring, medical decision support, and next-generation healthcare delivery. You’ll join a collaborative, mission-driven team where your expertise in agentic frameworks, machine learning, and scalable system design will play a vital role in shaping the future of intelligent healthcare solutions.

Familiarity with healthcare computational systems such as UMLS (Unified Medical Language System), CQL (Clinical Quality Language), and other health informatics standards is highly valued, as you’ll help bridge the gap between AI intelligence and clinical interoperability.

At Noble Cortex, we’re more than a startup studio—we’re a family office-backed venture focused on empowering visionary entrepreneurs to create the next big thing for the world, for humanity, and for themselves. Join us, and you’ll have the unique opportunity to work on transformative AI projects that have the potential to reshape industries and improve lives.

We foster a culture of innovation, collaboration, and personal growth. Our team is dedicated to turning bold ideas into reality, and we offer a space where creativity meets purpose.

We’re seeking a Machine Learning Engineer with a deep understanding of agentic frameworks and complex agentic system design, especially in decoupled, serverless environments. This role will play a pivotal part in building the core intelligence that powers adaptive AI agents in healthcare applications, including remote patient management, health monitoring, and real-time decision support.

You’ll work directly with product leadership, fellow engineers, and external partners to design, develop, and refine modular, scalable, and compliant systems that interpret and act on health data streams, patient input, and contextual signals. This is a highly strategic and hands-on position with the potential to impact how next-generation healthcare services are delivered at scale.

• Architect, build, and optimize agentic AI frameworks tailored to healthcare use cases, including care coordination, triage, and remote health monitoring.
• Design and deploy decoupled serverless systems that support multi-agent orchestration, real-time inference, and secure data interoperability.
• Develop ML-powered decision support modules that interpret patient data, clinical context, and medical knowledge bases to assist practitioners and caregivers.
• Collaborate cross-functionally to integrate AI agents with EHR systems, patient apps, and wearable/IoT health data sources.
• Train, fine-tune, and evaluate machine learning models for tasks such as time-series analysis, anomaly detection, reinforcement learning, and decision support in clinical contexts.
• Leverage healthcare ontologies, terminologies, and logic languages (e.g., UMLS, SNOMED, CQL, FHIRPath) to enhance AI interpretability and interoperability with medical systems.
• Work with compliance and product teams to ensure HIPAA-compliant, privacy-preserving data handling and deployment practices.
• Contribute to the development of internal tools and frameworks to accelerate experimentation, validation, and deployment cycles for new AI agents.
• Maintain documentation and contribute to knowledge sharing around best practices in scalable ML design, monitoring, and healthcare system integration.

• 4+ years of experience as a Machine Learning Engineer, with demonstrable work on agentic systems and frameworks.
• Proficient in Python and modern ML libraries (PyTorch, Tensor Flow, JAX).
• Deep experience with serverless architectures (e.g., AWS Lambda, Step Functions, API Gateway, Dynamo
  
  DB).
• Familiarity with microservices, event-driven systems, and data streaming (e.g., Kafka, Kinesis).
• Strong understanding of RL, multi-agent systems, and stateful…