Staff AI Machine Learning Engineer

Medeloop is seeking a Staff Machine Learning Engineer with deep expertise in agentic AI to design, build, test, evaluate, and productionize next-generation autonomous AI agents for healthcare and clinical research. The role involves leading the development of advanced AI systems, driving model development, and shaping the company's AI strategy in collaboration with multidisciplinary teams.

Responsibilities:

• Lead the design and architecture of advanced agentic AI systems, including reasoning loops (ReAct, CoT, ToT), tool-calling, dynamic multi-agent orchestration, RAG pipelines, memory/state management, and emerging protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A)
• Build and own production-grade agent infrastructure, including prompts, function tools, workflow graphs, MCP/A2A integrations, and adaptive agent lifecycle management (spinning up, specializing, delegating, and decommissioning agents dynamically for complex healthcare workflows)
• Develop rigorous evaluation and safety frameworks — automated testing, benchmarking, regression testing, adversarial testing, safety guardrails, observability (tracing, logging, metrics), and human-in-the-loop mechanisms to ensure reliable, compliant performance in production
• Drive LLM and ML model development — train, fine-tune, and deploy large-scale models on healthcare datasets, working closely with researchers and clinicians to solve real clinical challenges
• Shape Medeloop’s agentic AI strategy and roadmap in close partnership with the C-suite and cross-functional leadership
• Stay at the cutting edge of agentic AI (multi-modal agents, advanced reasoning models, interoperability protocols) and help establish Medeloop as a leader in transparent, compliant healthcare AI

Requirements:

• 7+ years of hands-on experience as a Machine Learning Engineer, with a proven track record building and shipping production agentic AI systems (single- or multi-agent) in industry, ideally in healthcare, life sciences, or other related domains
• Experience working on analytic engines (or advanced analytics platforms) — designing, optimizing, or integrating systems that power data-driven insights, queries, or decision-making at scale
• Strong theoretical foundation in ML/AI, with emphasis on NLP/LLMs, reinforcement learning, planning/reasoning algorithms
• Deep expertise with agentic frameworks and tools: LangChain/LangGraph, Model Context Protocol (MCP), Agent-to-Agent (A2A) protocols, Hugging Face, PyTorch, vector databases/semantic search, prompt engineering, and observability platforms (e.g., LangSmith, Phoenix)
• Experience designing fully automated evaluation and testing pipelines for autonomous agents and their orchestration, including metrics for reliability, safety, factuality, cost/latency, clinical utility, and dynamic behaviors
• A builder/experimenter mindset — you thrive on rapid prototyping, testing bold new ideas, iterating quickly on agent designs, and exploring uncharted territory in agentic systems
• Passion for unsolved challenges in healthcare AI, with the ability to thrive in a fast-paced, multidisciplinary environment and wear multiple hats
• Strong record in top AI/ML conferences/journals; experience with healthcare data (EHRs, claims) and regulatory considerations (HIPAA, transparency, reproducibility)
• Multi-cloud experience (AWS, Azure, GCP)