Senior AI Systems Quality Engineer

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About the Role

At Abacus Insights, we’re building AI‑powered platforms that sit at the center of critical healthcare decisions—where reliability, correctness, and trust matter as much as innovation. As our use of agentic, LLM‑driven systems scales, ensuring these systems behave safely and predictably in real‑world environments isn’t optional—it’s foundational.

The Senior AI Systems Quality Engineer plays a critical role in making our AI systems production‑ready, operable, and trustworthy. This is not a traditional QA role. Instead, you’ll help define how quality is engineered into agentic systems from the very beginning—embedding guardrails, evaluation signals, and safe‑failure behavior directly into how these systems are designed, built, and deployed. In this role, you will also help design and own an AI testing platform that operates natively within our Databricks Medallion architecture—leveraging tools like MLflow to enable evaluation, lineage, traceability, and auditability at scale. This is a platform-oriented role, focused on establishing repeatable, scalable quality practices rather than point-in-time validation.

You’ll partner closely with AI Engineers, platform teams, and delivery stakeholders to design custom evaluation frameworks, validation pipelines, and automated testing harnesses that reflect how agentic systems behave in production. You’ll focus on validating orchestration logic, managing non‑deterministic behavior, and ensuring AI systems degrade safely under uncertainty, scale, and failure.

This role is automation-first by design. You will build end-to-end automated AI testing integrated into the development lifecycle, where validation is continuously enforced—not manually reviewed after the fact. Your work will directly influence release readiness, operational confidence, and Abacus’ ability to responsibly deploy advanced AI in mission‑critical healthcare environments.

If you believe quality is a core engineering discipline—not a downstream checkpoint—this role gives you the opportunity to shape how reliable, governed, and scalable agentic AI is built from the ground up.

Your day to day

• 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.

What you bring to the team

• 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

What we would like to see, but not required

• Experience with Databricks-native environments and Medallion architecture.
• Experience using MLflow for model evaluation, lineage tracking, and auditability.
• Exposure to observability tools (e.g., Datadog, Prometheus, Grafana) for distributed AI workflows.
• Familiarity with LLM evaluation techniques, guardrails, and policy enforcement frameworks.
• Experience evaluating performance, latency, or cost regressions in AI systems.
• Ability to clearly document system behavior and quality trade-offs for technical and business audiences.
• Formal training or certification in AI/ML systems (e.g., ISTQB AI Testing, AWS ML Specialty, Google ML Engineer).
• Experience designing and iterating on prompts, agent behaviors, and orchestration logic as versioned, testable artifacts, enabling rapid refinement of AI system behavior (often referred to as “vibe coding”) without heavy reliance on traditional code changes.
• Familiarity with using AI systems to generate and expand test scenarios, including creating large-scale, diverse, and adversarial test datasets to significantly improve coverage and validation depth.

Compensation: Compensation for this role is based on experience, skills, and location, and includes base salary plus eligibility for performance bonuses and equity grants.

What you’ll get in return:

• 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#

Our Commitment as an Equal Opportunity Employer

As a mission-led technology company helping to drive better healthcare outcomes, Abacus Insights believes that the best innovation and value we can bring to our customers comes from diverse ideas, thoughts, experiences, and perspectives. Therefore, we dedicate resources to building diverse teams and providing equal employment opportunities to all applicants. Abacus prohibits discrimination and harassment regarding race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.

At the heart of who we are is a commitment to continuously and intentionally building an inclusive culture—one that empowers every team member across the globe to do their best work and bring their authentic selves. We carry that same commitment into our hiring process, aiming to create an interview experience where you feel comfortable and confident showcasing your strengths. If there’s anything we can do to support that—big or small—please let us know.

AI Use in Recruitment

We use AI-powered tools throughout our recruitment process. This includes Greenhouse's Real Talent Matching Technology, which helps prioritize applications based on job-related criteria, as well as additional AI tools used to support sourcing, interview preparation, and other recruiting tasks. We do not remove the human element—our recruiters and hiring teams review all applications and make all decisions related to candidate progression and hiring, regardless of which tools are used to support that process.

Identity and Location Verification

To protect the integrity of our hiring process, all interviews are conducted by live video with your camera on. Virtual or blurred backgrounds and headsets are not permitted absent an approved accommodation, and you should be in a well-lit, private space where your face and surroundings are clearly visible. Candidates who advance may also be asked to complete identity and location verification, which may include verifying your identity, work history, and confirming that you are located where your application states. Applications containing knowingly false or materially misrepresented information, including identity, location, or credentials, may be disqualified, and any offer may be rescinded if a material misrepresentation is discovered at any point in the process.

If you need an accommodation for any part of the interview process, please let your recruiter know in advance.

By applying, you acknowledge that we will collect and process your personal information for recruiting purposes in accordance with applicable data protection laws, including for the verification steps described above. Please review ourApplicant Privacy Noticefor more information, including your rights.