Lead Software Quality Engineer - Remote

UnitedHealth Group•Eden Prairie, MN

1d•Remote

About The Position

Optum Tech is a global leader in health care innovation. Our teams develop cutting-edge solutions that help people live healthier lives and help make the health system work better for everyone. From advanced data analytics and AI to cybersecurity, we use innovative approaches to solve some of health care’s most complex challenges. Your contributions here have the potential to change lives. Ready to build the next breakthrough? Join us to start Caring. Connecting. Growing together. Information is the lifeblood of the healthcare industry—everything depends on it. At Optum Insight Technology, you’ll help us work on streamlining the flow of information between payers, healthcare providers and various other stakeholders to deliver the right insights to the right places at the right times, driving better outcomes for patients, reducing friction in the health system and lowering costs. Every day our work directly impacts the world for the better, in meaningful and profound ways. We live in a time of unprecedented technical capability and possibility. Health care is at a pivotal point in this journey where even small gains can lead to major transformation. You could be a part of that – you have tremendous skill and the potential to make a lasting impact. Optum Insight Technology is uniquely positioned to bring your skills to bear on these pressing and life-changing technical challenges. The health care industry has an immediate need for your drive, innovation, passion and technical insight. Help us help the millions of people we serve each day. The Clinical Decision Support (CDS) Engineering team, a unit within the Optum Insight Technology organization is responsible for building commercial products helping payers and providers with administrative and clinician focused CDS solutions. We are seeking a Lead Software Quality Engineer to help ensure the reliability, safety, and effectiveness of a mission critical, AI/ML enabled clinical software platform. This role will partner closely with Product Management, Software Engineering, Data Engineering, and ML teams to identify the highest risk areas of the product and guide teams in building robust, scalable, and risk focused test automation. CDS is an area that truly touches payers, providers, as well as members and one where you can have a huge impact on improving the healthcare system for everyone. This role is ideal for a quality leader who blends deep technical testing expertise, systems thinking, and a risk based mindset, and who is excited about applying AI/ML techniques to modernize test automation. You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges. For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, or equivalent practical experience
6+ years in software quality engineering, test automation, or software engineering with a solid quality focus, including ownership of automation for complex systems
3+ years professional software development in .NET with particular attention to test and automation frameworks
3+ years of experience in front-end development and testing with JavaScript and Angular and/or React, including cross-browser testing
3+ years of experience operating at a senior/principal/lead level, influencing quality strategy across multiple teams and driving outcomes through influence
Solid understanding of CI/CD, modern DevOps practices, and cloud-native architectures; ability to integrate quality signals into pipelines and release gates
Demonstrated expertise designing automation frameworks for distributed systems (APIs/services) and data-intensive platforms
Demonstrated excellent communication skills with a track record of translating quality risk into clear decisions for technical and non-technical stakeholders

Nice To Haves

Hands-on use of AI Coding Tools (Claude, Codex, CoPilot) and related techniques to improve testing (e.g., intelligent test selection, test generation/maintenance, failure clustering) and remediate bugs faster
Hands-on experience with Python and Python test frameworks
Experience delivering quality for AI/ML-enabled products, ideally in healthcare or another regulated domain
Experience with data ingestion/validation, analytics testing, and data quality observability.
Experience validating security controls (RBAC/authorization) and supporting audit/control requirements
Familiarity with observability, incident learnings, and production quality signals for complex platforms

Responsibilities

Partner with Product, Engineering, Data Engineering, and ML teams to identify, quantify, and continuously reassess the highest functional, clinical, data, operational, and security risks across the platform
Define and socialize a risk-based quality strategy (test scope, depth, automation targets, environments, and gates) aligned to patient impact and business priorities
Provide quality leadership for release readiness by defining meaningful quality signals/metrics, communicating tradeoffs, and influencing go/no-go decisions
Own the technical direction for automation frameworks and test infrastructure across layers (data pipelines, APIs/services, AI/ML decision workflows, admin/config portals, and analytics/reporting)
Establish quality engineering standards (test pyramid strategy, coding patterns, reliability, and maintainability) and drive consistent adoption across teams
Enable engineering teams to build, run, and own automated tests in CI/CD through coaching, reusable libraries, and guardrails (not by becoming a bottleneck)
Advance modern practices such as contract testing, data quality checks, and synthetic test data strategies
Partner with ML and data teams to define and automate validation of training/inference data quality, feature/label integrity, and model input/output edge cases
Work with architects and AI/ML engineers to define quality approaches for model monitoring (drift, bias/fairness as applicable, performance degradation) and ensure signals are actionable for operations and releases
Apply or experiment with AI-assisted testing (e.g., test generation/maintenance, intelligent test selection, anomaly detection) to improve automation effectiveness
Ensure quality practices support required traceability, auditability, and explainability for clinical decision workflows
Partner with security/compliance stakeholders to ensure test coverage supports HIPAA, SOC, and other applicable control requirements
Partner with product and engineering to ensure analytics/reporting correctly represents product usage, system health, and AI performance where applicable
Use trends and learning from incidents, defects, and telemetry to continuously improve test strategy, tooling, and release processes