Staff Software Development Engineer in Test

ComPsych Corporation•,

5d•$160,000 - $200,000

About The Position

ComPsych is modernizing its technology with new customer-facing web and mobile experiences, a unified design system, and AI-powered product capabilities. The Staff SDET will establish quality as an engineering discipline across teams, owning test strategy, automation, and signal quality without a manual QA layer. The role combines exploratory testing with maintainable automation and AI-assisted development, while ensuring that PHI never enters non-production environments. Platform owns environments and pipelines; this role owns the test configuration, data, and quality signals that run on them. This is a cross-team technical leadership role for an SDET who has built testing frameworks and standards adopted by multiple engineers and teams. The successful candidate will set testing direction, define how ComPsych tests software, and make effective practices easy to follow. They will work across Engineering, Product, Design, and Platform to translate product and architectural risks into verification strategies, improve feedback, and guide AI-first quality practices while retaining human judgment over evidence and generated tests.

Requirements

Strong skills in a modern programming language and ability to reason about concurrency, asynchronous workflows, and failure propagation across services.
Ability to trace failing tests across services using logs, traces, and deployment context and identify whether root causes lie in the product, automation, data, or environment.
Ability to clearly communicate residual risk to Engineering and Product, including when shipping with a known gap is appropriate.
Depth in at least one non-functional area: performance, resilience, security, or accessibility, with judgment about when to involve specialists.
Experience working AI-first, establishing conventions followed by other engineers, and explaining boundaries for agent changes that do not require human review.
Experience building a testing framework or shared automation platform adopted by multiple engineers or teams, with demonstrable impact on how they work.
Experience defining testing standards and best practices, such as test-layer guidance, flake policy, and gating criteria, and driving adoption without relying on authority.
Experience designing or evolving a quality system across teams, translating product and architectural risk into verification strategy, and explaining what was deliberately not tested.
Experience setting technical direction beyond one's own team and influencing roadmap, tooling, and tradeoffs across Engineering.

Nice To Haves

Experience verifying LLM-backed or other non-deterministic products.
Experience with synthetic-data strategy and deterministic isolation for sensitive data.
Experience with failure injection or recovery testing in production-like environments.
Experience in healthcare, another regulated domain, or systems that handle sensitive customer data.

Responsibilities

Build automation architecture, frameworks, fixtures, and shared standards that enable reliable parallel execution, accounting for concurrency, asynchronous behavior, and eventual consistency.
Set testing practices and drive adoption across teams through usefulness and influence.
Own quality strategy across teams by defining risk-based verification strategies, specifying the evidence needed at each test layer, directing exploratory testing toward unfamiliar failure modes, and documenting what is intentionally not tested and why.
Improve test feedback by owning execution speed, gating, failure classification, and flake discipline so developers can resolve failures themselves.
Define synthetic-data strategy, isolation patterns, and CI protections that keep PHI out of non-production environments and provide deterministic test state.
Define testability and non-functional quality requirements with Platform, including environment, observability, and data needs; set appropriate verification depth for performance, resilience, accessibility, and security.
Establish AI-first quality practices, author versioned and reviewable agent conventions, define which agent actions require human review, and evaluate generated tests as drafts until their evidence is trustworthy.
Trace failing tests across services using logs, traces, and deployment context, and distinguish product, automation, data, and environment causes.
Communicate residual risk to Engineering and Product, including when shipping with a known gap is appropriate.
Influence technical direction, roadmaps, tooling, and tradeoffs across Engineering, and mentor engineers in adopted standards.