Lyric is an AI-first, platform-based healthcare technology company, committed to simplifying the business of care by preventing inaccurate payments and reducing overall waste in the healthcare ecosystem, enabling more efficient use of resources to reduce the cost of care for payers, providers, and patients. Lyric, formerly ClaimsXten, is a market leader with 35 years of pre-pay editing expertise, dedicated teams, and top technology. Lyric is proud to be recognized as 2025 Best in KLAS for Pre-Payment Accuracy and Integrity and is HI-TRUST and SOC2 certified, and a recipient of the 2025 CandE Award for Candidate Experience. Interested in shaping the future of healthcare with AI? Explore opportunities at lyric.ai/careers and drive innovation with #YouToThePowerOfAI.
We're a company at the intersection of FinTech and Healthcare, committed to simplifying healthcare payment integrity and creating value for health insurers, healthcare providers, and consumers. We're not a startup, but we move like one when it matters. Right now, we're in the middle of a large modernization of our payment integrity platform: ripping out legacy .NET, rebuilding around clean APIs, fixing data bottlenecks, and getting the product ready for scale.
We're looking for a Engineering Manager to lead the engineering team behind this modernization. You'll own the delivery outcomes for a team of full-stack and backend engineers building modular Angular frontends and resilient .NET microservices – and you'll be hands-on enough to unblock architectural decisions, review critical PRs, and jump into code when the situation calls for it. You'll partner closely with product, UX, DevOps, and QA to keep the team shipping customer-delighting features at a pace that matches our ambition.
Here's what makes this role different: you're not managing a team that happens to use AI tools – you're building a team where AI-assisted engineering is the default way of working. You've used Cursor, Claude Code, Codex, or similar tools yourself, and you know how to coach engineers on using them effectively. You understand the difference between an engineer who uses AI to go faster and one who uses it to avoid thinking. Your job is to make sure the team stays on the right side of that line – and gets dramatically more productive in the process.
This is the core of the role:
Own the delivery roadmap for platform modernization – sequencing migration work, API extraction, and frontend decoupling into realistic, high-impact milestones.
Remove blockers before the team feels them. Whether it's a cross-team dependency, an unclear requirement, or a technical rabbit hole, you see it early and act on it.
Hold the team to high engineering standards without micromanaging: clean PRs, meaningful test coverage, current documentation, and architecture decisions that are recorded and revisited.
Get hands-on when it matters – reviewing critical design decisions, pairing on thorny migration problems, or stepping into code to unblock a stuck workstream.
What success looks like:
Modernization progresses predictably sprint over sprint. Legacy surface area shrinks measurably. Stakeholders trust the plan because the team keeps hitting its marks.
Engineering quality improves alongside velocity – production incidents trend down, not up.
Build and scale AI-assisted engineering practices
Set the standard for how the team uses AI coding tools. That means establishing repo-level agent instructions, prompt playbooks, review standards for AI-generated code, and clear expectations for when AI output needs human scrutiny (always).
Coach engineers individually on effective AI workflows – not just tool mechanics, but judgment: knowing when the agent is hallucinating, when to override, and when to step back and think before prompting.
Measure the impact. Track migration throughput, PR cycle times, test coverage growth, and defect rates to prove that AI-assisted practices are actually working – and course-correct when they aren't.
What success looks like:
The team ships meaningfully more, without a spike in bugs or rework.
AI practices are embedded in team culture, not dependent on you pushing them. New engineers ramp up on AI workflows within their first sprint.
Own the technical direction alongside your leads
Partner with senior engineers to make sound architectural calls on the modernization: service boundaries, API contracts, data migration strategies, event-driven patterns, and frontend decomposition.
Keep the team honest about tech debt. You'll maintain a living view of what's been modernized, what's next, and what's intentionally deferred – and make sure "deferred" doesn't quietly become "forgotten."
Stay close to the data layer challenges. You don't need to tune every query yourself, but you need to understand where the database hot spots are and make sure the team is addressing them systematically.
What success looks like:
Architecture decisions are well-documented, debated constructively, and revisited when assumptions change.
Critical paths live in modern code. The team spends less time working around legacy constraints and more time building forward.
Build and develop a high-performing team
Hire, retain, and grow strong engineers. You'll make staffing decisions, run effective 1:1s, give direct feedback, and create real growth paths – not just title bumps.
Foster a team culture of ownership, psychological safety, and honest communication. Engineers should feel comfortable raising concerns, pushing back on decisions, and admitting when something isn't working.
Balance workload across the team and across workstreams. You'll manage capacity proactively – not reactively after someone burns out or a workstream stalls.
What success looks like:
Attrition is low because people want to be here, not because they're stuck. Engineers on your team grow visibly in capability and scope.
The team is self-organizing on day-to-day work. You're spending your time on the problems only a manager can solve.
Ensure delivery in a regulated environment
Make security, privacy, and compliance non-negotiable in how the team builds. Least privilege, safe data handling, audit trails – these are engineering defaults.
Put real quality guardrails in place and enforce them: CI that blocks bad changes, automated tests that catch regressions, structured logging, and observability that surfaces issues before customers do.
Own incident response for your team's domain. When something breaks, you drive root cause analysis and make sure fixes are durable, not duct tape.
What success looks like:
Fewer severity-1 incidents. When something does go wrong, root cause takes hours, not days.
Compliance and security reviews don't slow the team down because the foundations are already solid.
Partner across the organization to ship things that matter
Work with product, analysts, and business stakeholders to translate business priorities into engineering plans with honest trade-offs and pragmatic milestones.
Represent engineering in cross-functional planning. You'll push back when timelines are unrealistic and commit when they're achievable – and the team will trust your judgment on the difference.
Communicate up and out clearly: delivery status, risks, staffing needs, and technical direction – without requiring anyone to ask twice.
What success looks like:
REQUIRED QUALIFICATIONS
8+ years in software engineering, with at least 3 years in engineering management – leading teams, not just projects. You've managed hiring, performance, and delivery for teams of 5+ engineers.
5 + years in C# and modern .NET (Core and beyond). You've personally done meaningful refactoring and migration work in large codebases. You're not managing from a spec – you can read the code, spot problems in a PR, and have credible architectural opinions.
8 + years SQL Experience – schema design, query tuning, performance troubleshooting. You've managed teams working on data-heavy applications and you understand the database challenges firsthand.
You actively use AI coding tools – Cursor, Claude Code, Codex, or similar – and you've coached others on using them effectively. You have a point of view on how AI tools change engineering workflows, review practices, and team productivity.
PREFERRED QUALIFICATIONS
Experience with distributed systems – Kafka, event-driven architectures, or hybrid database environments (SQL Server + Postgres).
Familiarity with Angular and/or React/Next.js – enough to guide frontend architecture decisions and review frontend work meaningfully.
You've built AI-assisted engineering practices at the team level: repo instructions for agents, CI guardrails for AI-generated code, task decomposition patterns, and ways to measure the productivity impact.
Experience with progressive delivery – feature flags, canary deployments, blue-green – and you've used these to de-risk large migrations.
You communicate clearly and lead with low ego. Straightforward status updates, honest trade-off conversations, direct feedback delivered with respect. You build trust by being consistent and transparent.
Background in regulated domains (healthcare, fintech, payments) where compliance isn't optional and "move fast and break things" will get you fired.
***The US base salary range for this full-time position is:
$150,289.00 - $225,434.00
The specific salary offered to a candidate may be influenced by a variety of factors including but not limited to the candidate’s relevant experience, education, and work location. Please note that the compensation details listed in US role postings reflect the base salary only, and does not reflect the value of the total rewards compensation. ***
Lyric is an Equal Opportunity Employer that strives to create an inclusive environment, empower employees and embrace collaborative success.
