Assured is transforming the infrastructure of U.S. healthcare using intelligent automation. We’re building an AI-native system of action for provider operations to automate the most painful parts of healthcare, starting with credentialing, licensing, and payer enrollment. These are slow, error-prone processes that cost the healthcare system billions and delay patient care.
We’re backed by top Silicon Valley investors and trusted by the most innovative provider groups and health systems. This is a rare opportunity to join an elite team reimagining one of the most broken parts of healthcare - using cutting-edge AI in the real world, at scale.
We’re looking for an Engineering Lead to help architect, build, and productize our cutting-edge provider operations platform. You'll drive the development of a resilient, modular platform that supports mission-critical healthcare workflows at global scale.
This role is ideal for someone who thrives in early-stage environments, enjoys owning things end-to-end, and wants their work to have a measurable impact on an industry that desperately needs modern infrastructure.
System Architecture & Design
Design and evolve a modular microservices-based architecture with clear domain boundaries and scalable APIs.
Lead the adoption of publish/subscribe (pub/sub) patterns for real-time, decoupled communication using platforms such as Kafka or Amazon SQS.
Architect systems to support high availability, disaster recovery, and performance optimization at scale.
Define cloud infrastructure architecture using AWS, GCP, or Azure, with a focus on security, scalability, and automation.
Artificial Intelligence & Innovation
Lead the strategy, design, and integration of Generative AI and Machine Learning capabilities to enhance clinical decision support, automate operational workflows, and personalize user experiences.
Evaluate AI tools, frameworks, and third-party APIs, ensuring alignment with platform goals and data protection requirements.
Technical Leadership
Provide architectural guidance across teams and ensure adherence to engineering best practices throughout the SDLC.
Mentor software engineers, promote a culture of technical ownership, and facilitate design reviews and documentation.
Guide teams in deploying scalable, observable, and resilient services with CI/CD pipelines, infrastructure-as-code, and monitoring tools.
Security, Compliance & Governance
Champion best practices for system security, auditability, and privacy by design.
Lead efforts in incident response readiness, chaos engineering, and platform reliability.
Collaboration & Stakeholder Engagement
Act as a technology advisor to product managers, business leaders, and clinical stakeholders.
Translate complex business requirements into scalable technical solutions that align with product vision and regulatory standards.
Must-Have:
8+ years of experience designing and scaling distributed systems and SaaS platforms in regulated environments (e.g., healthcare, finance).
Proven expertise in microservices architecture, asynchronous messaging systems, and container orchestration using Kubernetes.
Proficiency in modern programming languages (e.g., Node.js, Go, Python) and infrastructure-as-code tools (e.g., Terraform, Pulumi).
Demonstrated leadership in implementing AI/ML and GenAI solutions in production.
Proven ability to drive architecture and own end-to-end systems delivery
Great communication—able to explain technical trade-offs to technical and non‑technical audiences
Preferred:
MS/PhD in CS, ML, NLP, or related
Experience in healthcare, insurance, compliance domains
Prior experience deploying enterprise-grade systems
Open-source contributions, publications, or technical blog presence
You’ll Love This Role If You
Want to lead projects from idea to deployment
Thrive in a 0-to-1 environment and like building from scratch
Care about real-world impact, especially in healthcare
Enjoy building systems—not just training models
Believe great products come from close collaboration with product, engineering, and users
High-impact work - Tackle bottlenecks that slow down provider access to patients
Real-world AI - Work on meaningful applications of LLMs and applied ML in compliance, forms, automation, and document intelligence
Cross-functional exposure - Collaborate with customers, clinical ops, engineers, and founders
Early-stage upside - Equity, early influence, and a high-growth trajectory
People-first culture - Remote flexibility, mental health time, and a focus on outcomes, not hours
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