[Remote] Staff AI Machine Learning Engineer
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is seeking a Staff Machine Learning Engineer with deep expertise in agentic AI to design, build, test, evaluate, and productionize reputed company autonomous AI agents for reputed company 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
- reputed company 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 reputed company reputed company workflows)
- reputed company rigorous evaluation and safety frameworks — automated testing, benchmarking, regression testing, adversarial testing, safety guardrails, observability (tracing, logging, metrics), and reputed company-in-the-reputed company mechanisms to ensure reliable, compliant performance in production
- Drive LLM and ML model development — train, fine-tune, and reputed company large-scale models on reputed company datasets, working closely with researchers and clinicians to solve reputed company clinical challenges
- Shape reputed company’s agentic AI strategy and roadmap in reputed company 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 reputed company as a leader in transparent, compliant reputed company AI
Skills
- 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 reputed company, life sciences, or other reputed company 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 reputed company in ML/AI, with emphasis on NLP/LLMs, reinforcement learning, planning/reasoning algorithms
- Deep expertise with agentic frameworks and tools: reputed company/LangGraph, Model Context Protocol (MCP), Agent-to-Agent (A2A) protocols, reputed company, PyTorch, vector databases/semantic search, reputed company 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 reputed company — you reputed company on rapid prototyping, testing reputed company new reputed company, iterating quickly on agent designs, and exploring uncharted territory in agentic systems
- Passion for unsolved challenges in reputed company AI, with the ability to reputed company in a fast-paced, multidisciplinary environment and wear multiple hats
- Strong record in top AI/ML conferences/journals; experience with reputed company data (EHRs, claims) and regulatory considerations (HIPAA, transparency, reproducibility)
- Multi-reputed company experience (AWS, Azure, GCP)
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