Senior Engineering Manager

ComPsych Corporation•,

7d•$225,000 - $275,000

About The Position

We're modernizing how ComPsych builds technology — new customer-facing web and mobile experiences, a unified design system, and AI-powered capabilities across our products. That means raising the engineering bar for how we design, build, and operate software, with Engineering, Product, and Design working together end to end. AI is part of how we engineer, not a productivity add-on. Our engineers use agentic development as standard equipment — writing clear specs, delegating meaningful work to AI agents, and applying strong technical judgment to what comes back. Our platform carries behavioral health data for millions of members, so reliability, privacy, and correctness are non-negotiable — not a tax on velocity. As a Senior Engineering Manager, your organization is your product: you're accountable for what multiple teams ship, how the group operates as AI changes the job, and the managers and engineers who grow under your leadership. Your job isn't to run any one team day to day — it's to create the conditions, standards, and talent pipeline that let several strong teams move with high ownership and autonomy.

Requirements

You've led engineers and managers through the hard parts of scaling an organization. You've set expectations, coached people at different levels, managed performance, supported career growth and promotions, and handled difficult conversations directly — across more than one team.
You have technical depth engineers and managers trust, across more than one technical domain. You can engage meaningfully in design reviews for Java, Python, C#, or similar systems, challenge API contracts and data models, and reason about failure modes and testing strategy well enough to hold other managers accountable for the same rigor.
You've owned production engineering across multiple teams running on AWS or Azure. You've been accountable for observability, SLOs, on-call, incident response, and postmortem follow-through at a scale beyond a single team's services.
You know how to lead an organization that builds secure, privacy-sensitive systems. You can challenge authentication and authorization decisions, ensure threat modeling and sensitive-data handling are addressed up front across teams, and drive security findings through remediation.
You run engineering with evidence, not anecdotes, at the organizational level. You've used delivery, quality, reliability, and team-health signals — cycle time, deployment frequency, change failure rate, escaped defects, MTTR, or operational load — to compare teams fairly, decide where to intervene, and report meaningful progress to leadership.
You improve the system instead of becoming the system, at a scale beyond one team. You create teams and managers who can make decisions and move independently, remove organizational friction a single EM can't fix alone, and treat dependency on you as something to eliminate, not evidence of your value.
You've led AI-first engineering in practice, and shaped how other managers do the same. You've directed agents on real work, established how engineers delegate and verify meaningful implementation, and helped other managers adapt their teams' operating model as the bottleneck moved from implementation to specification, review, or decision-making.

Nice To Haves

Experience with PHI/HIPAA or comparable regulated environments is preferred.
Bonus: experience managing other engineering managers, leading an organization through a similar AI-native transition, or shaping hiring and leveling criteria at the org level.

Responsibilities

Build and grow a strong engineering organization, not just a single team. Create clarity, accountability, and growth so multiple teams — and the managers or leads within them — can take on increasing scope and perform at a high bar.
Create a credible delivery system across teams. Make priorities, ownership, dependencies, and capacity visible across the group you lead, and resolve the tradeoffs that no single team can make on its own.
Partner with Product and Design leadership on outcomes and execution at the domain level. Get pulled into discovery early, turn business priorities into executable plans across teams, and negotiate scope directly when AI-driven planning speed outpaces what engineering can safely validate and ship.
Hold the engineering bar across your organization. Ensure important technical decisions get the right depth of review everywhere you're accountable, and build the standards that make good judgment repeatable without your direct involvement in every decision.
Own the health of what your organization puts into production. Make sure every team you're accountable for can operate what it ships and respond effectively when things fail, and use delivery, quality, and reliability signals to know where to intervene and fix structural causes rather than recurring symptoms.
Set how your organization works with AI, and shape talent and standards around it. Define the delegation, verification, and review practices multiple teams adopt, evolve hiring and leveling criteria to reflect AI-native skills, and mentor other managers on coaching their engineers through the same shift.