Lead AI Applied Engineer

About this role

Become a part of our caring community   You have shipped AI products before. You understand the difference between a demo and a production system. You have strong opinions about evaluation frameworks because you have experienced the consequences of operating without them. You are at your best when you own architecture decisions while continuing to build and deliver critical code yourself.

We build the platform that transforms millions of clinical documents into trusted, actionable data. Our systems use large language models (LLMs) to read medical records, extract structured facts, answer complex questions with citations back to source documents, and route complex cases to human experts. The output of these systems supports healthcare decisions that impact real members.

As a Lead AI Applied Engineer, you will provide technical leadership for AI-enabled products and platforms, define architectural direction, establish engineering standards, and personally design and build the most critical components of our systems. You will lead through both technical expertise and execution, helping the team deliver reliable, scalable, and auditable AI solutions in a highly regulated healthcare environment.

Why Join Us • Lead the architecture of production AI systems where LLMs are foundational to the product experience. • Make key technical decisions regarding model selection, system boundaries, platform architecture, and build-versus-buy strategies. • Own the highest-risk and highest-impact technical challenges involving reliability, explainability, and correctness. • Influence engineering culture and establish standards that shape how the team builds and ships AI products. • Work on systems operating at meaningful scale, processing millions of documents and supporting healthcare decisions across a large member population. • Partner with highly skilled engineers while remaining deeply hands-on in coding, design, and production operations. • Simplify complex solutions and drive pragmatic engineering decisions that maximize business value.

Key Responsibilities • Own the architecture, design, and evolution of full-stack AI applications, including LLM pipelines, retrieval systems, agentic workflows, human-in-the-loop processes, and supporting platform services. • Design and implement scalable AI solutions that prioritize reliability, accuracy, auditability, performance, and cost efficiency. • Build and maintain the most complex and high-risk system components where architecture and implementation decisions have significant business impact. • Define and enforce engineering standards for AI systems, including evaluation methodologies, structured outputs, observability, testing, fallback strategies, latency optimization, and cost controls. • Lead technical design reviews and guide architecture decisions related to platform capabilities, AI systems, integrations, infrastructure, and software patterns. • Evaluate and recommend technologies, frameworks, AI models, and third-party solutions based on technical and business requirements. • Translate ambiguous business goals into clear technical strategies, roadmaps, and executable workstreams. • Provide technical leadership across multiple projects, ensuring alignment with architectural standards and long-term platform objectives. • Mentor and coach engineers through design reviews, code reviews, pair programming, and technical guidance. • Partner with product, engineering, clinical, and operational stakeholders to deliver solutions that meet business and regulatory requirements. • Own operational excellence, including deployment strategies, monitoring, incident management, and production reliability. • Ensure all solutions comply with privacy, security, governance, and audit requirements within a regulated healthcare environment.

Use your skills to make an impact   Required Qualifications • Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.

• 8+ years of software engineering experience, including experience designing and operating production systems at scale.

• Proven track record of delivering AI-enabled products or platforms into production environments.

• Deep hands-on experience integrating and operating LLMs within business-critical workflows.

• Experience designing systems that leverage structured outputs, tool calling, retrieval-augmented generation (RAG), agentic workflows, orchestration frameworks, and evaluation pipelines.

• Experience making architectural decisions for systems where AI is a core component of the product experience.

• Demonstrated success leading technical initiatives across engineering teams, including architecture reviews, technical planning, mentoring, and delivery execution.

• Strong programming skills in Python and/or TypeScript/JavaScript.

• Experience designing and operating distributed systems, APIs, data platforms, and cloud-native applications.

• Strong understanding of reliability engineering, system performance, scalability, and operational excellence.

• Ability to balance technical tradeoffs involving quality, latency, cost, security, and maintainability.

Preferred Qualifications • Experience taking AI products from concept through production deployment and long-term operational ownership.

• Experience developing AI systems where LLMs are part of critical decision-support or operational workflows.

• Expertise in evaluation frameworks, benchmark datasets, regression testing, model quality measurement, and human review processes.

• Experience with agentic systems, Model Context Protocol (MCP), workflow orchestration, multi-step reasoning frameworks, and AI observability platforms.

• Experience with React, Next.js, modern frontend technologies, and full-stack application development.

• Experience deploying and managing workloads using Kubernetes, Docker, modern CI/CD platforms, and cloud-native technologies.

• Experience with Google Cloud P

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