Senior AI Systems Quality Engineer

Abacus Insights

Senior AI Systems Quality Engineer ensuring reliability and trustworthiness of AI systems for healthcare decision-making. Building automated frameworks and validation protocols in a production environment.

Posted 5/18/2026full-timeRemote • Massachusetts • 🇺🇸 United StatesSeniorWebsite

Tech Stack

Tools & technologies

AWSCloudDistributed SystemsPythonTypeScript

About the role

Key responsibilities & impact

Build and ship production-grade, automated validation frameworks, test harnesses, and evaluation pipelines across the AI lifecycle (design → deploy).
Design and evolve an AI testing platform integrated with Databricks and MLflow, enabling repeatable testing, traceability, and auditability.
Create large-scale, scenario-based test suites (hundreds to thousands of cases) to validate agentic workflows end-to-end, including edge cases, long-tail scenarios, and failure modes.
Validate orchestration behavior (tool use, memory, decision logic) and stress-test non-deterministic system behavior before production.
Embed quality by design: define system contracts, guardrails, and safe-degradation patterns at key boundaries.
Define measurable quality signals for LLM systems (grounding, hallucinations, relevance, latency, cost) and integrate them into CI/CD pipelines as automated quality gates.
Ensure AI validation runs automatically on model, prompt, and code changes—enabling continuous quality enforcement.
Build reusable libraries and components so teams can adopt consistent AI quality practices quickly.
Own aspects of AI release readiness, including defining go/no-go criteria based on measurable quality thresholds.
Partner with AI, platform, security, and delivery teams to translate mission needs into clear quality criteria, tradeoffs, and confidence levels.

Requirements

What you’ll need

7+ years of software engineering experience, primarily in backend or platform systems.
Proven experience designing and implementing AI testing automation in production environments, not just executing tests.
Demonstrated ability to build custom validation, evaluation, or testing frameworks for complex, distributed systems.
Strong proficiency in Python and/or TypeScript within modern AI engineering stacks.
Hands-on experience with AI-powered systems, including LLM-based or agentic workflows and non-deterministic behavior.
Experience designing or contributing to AI testing at scale, including regression frameworks, long-tail evaluation, and large test coverage.
Deep understanding of CI/CD integration, including embedding automated tests and quality gates into deployment pipelines.
Solid understanding of AWS cloud-native architectures.
Track record of engineering for quality, reliability, governance, and safety as core system design principles.
Working knowledge of security, privacy, and operational risk in regulated or mission-critical environments, including failure modes and recovery.
Experience with AI testing methodologies, including evaluation of non-deterministic outputs, drift detection, bias/fairness testing, and robust regression strategies.
Proven ability to establish measurable trust thresholds for AI systems, including defining and operationalizing success metrics such as query accuracy, hallucination limits, explainability, and PHI-safe behavior as enforceable release criteria.
Experience working with domain experts to define correctness and real-world validation scenarios, enabling large-scale, business-relevant test coverage that reflects true production use cases rather than engineering-only perspectives.

Benefits

Comp & perks

Unlimited paid time off – recharge when you need it
Work from anywhere – flexibility to fit your life
Comprehensive health coverage – multiple plan options to choose from
Equity for every employee – share in our success
Growth-focused environment – your development matters here
Home office setup allowance – one-time support to get you started
Monthly cell phone allowance – stay connected with ease

ATS Keywords

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Hard Skills & Tools

PythonTypeScriptAI testing automationvalidation frameworksevaluation frameworksCI/CD integrationAWS cloud-native architecturesregression frameworksdrift detectionbias testing

Soft Skills

engineering for qualityreliabilitygovernancesafetycollaborationcommunicationproblem-solvingcritical thinkingadaptabilityleadership