Senior Software & AI Test Engineer
Listed on 2026-07-21
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Software Development
AI QA / Validation Engineer, Software Testing
We are hiring a hands‑on Senior Software & AI Test Engineer to design and operationalize a scalable, automation‑first quality framework across our software and AI‑driven systems. This role owns test strategy, infrastructure, and execution, ensuring high‑confidence releases across APIs, cloud services, data pipelines, and AI/ML components.
The mandate is twofold: (1) build robust, modern testing systems and (2) embed a pragmatic culture of quality that keeps pace with rapid product development.
Key Responsibilities1) Test Architecture & Infrastructure
- Design and implement a unified test framework across backend services and APIs, cloud platforms and distributed systems, data pipelines and data quality layers, and AI application and evaluation systems.
- Define test environments, mocking/simulation strategies, and synthetic data generation.
- Build and maintain CI/CD pipelines.
- Integrate testing deeply into CI/CD pipelines with clear gating signals.
- Build and maintain automated test suites: unit, integration, system, regression, and performance testing.
- Develop test orchestration, reporting dashboards, and failure triage workflows.
- Ensure tests are deterministic, reproducible, and fast enough for developer iteration.
- Establish validation strategies for data pipelines: schema validation, anomaly detection, and data integrity checks.
- Build automated tests for ETL workflows and downstream system dependencies.
- Ensure reproducibility between offline experimentation and production behavior.
- Lead investigation of complex failures across services, data, and AI layers.
- Establish structured approaches to failure classification and regression prevention.
- Build continuous evaluation pipelines tied to model releases.
- Define acceptance criteria and release gates for AI features.
- Develop benchmarking tools for comparing models across datasets and scenarios.
- Introduce scalable quality practices: shift‑left testing and testability in design.
- Definition of done includes validation and observability.
- Partner with engineering and product to define measurable quality metrics (defect escape rate, test signal quality) and release criteria aligned with risk.
- Balance thoroughness with speed—avoid over‑engineering test systems.
- 6–10+ years in software testing, SDET, or quality engineering.
- Strong programming skills (Python required; experience with backend systems preferred).
- Proven track record building test frameworks and automation from scratch.
- Deep understanding of API testing, distributed systems, cloud architectures, CI/CD systems (e.g., Git Hub Actions, Jenkins), test methodologies (boundary testing, fuzzing, fault injection).
- Hands‑on experience validating AI/ML systems, including model evaluation metrics, dataset validation, experiment tracking tools (e.g., MLflow, Weights & Biases).
- Experience with LLM or computer vision evaluation is a plus.
- Experience testing data‑intensive systems or analytics platforms.
- Familiarity with data engineering tools and workflows.
- Experience with performance, scalability, and reliability testing.
- Exposure to observability tooling (logs, metrics, tracing) for test validation.
- Experience working in fast‑paced product environments with evolving requirements.
- Design and implement evaluation frameworks for AI/ML systems: model performance, robustness, edge cases, failure modes, data quality, drift detection, dataset versioning.
- Builder mindset:
Creates frameworks and tools, not just test cases. - Systems thinker:
Understands interactions across APIs, data, and AI layers. - Pragmatic operator:
Applies the right level of rigor for the stage of product maturity. - Quality driver:
Elevates engineering standards without becoming a bottleneck. - Hands‑on depth:
Writes code, debugs systems, and owns outcomes.
- Production‑grade automated test framework integrated into CI/CD.
- Clear, adopted AI evaluation framework used across AI application releases.
- Reduction in escaped defects and regression incidents.
- Measurable improvement in data pipeline reliability and validation coverage.
- Increased developer confidence in releases without slowing iteration speed.
At Covalent, you’ll work alongside world‑class scientists and engineers in a dynamic, collaborative environment. We empower our team members to take ownership of their work, innovate constantly, and engage directly with customers, shaping the future of technology.
Compensation125,000 – 175,000 USD per year (Covalent Sunnyvale)
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