QA Data Science Engineer

Come work at a place where innovation and teamwork come together to support the most exciting missions in the world!

Job Description

We are seeking a Data Science focused QA engineer to develop next-generation Security Analytics products. You will work closely with Data scientists, engineers and product managers to design and optimize AI driven security solutions.

As QA engineer, the ideal candidate has a strong background in Backend engineering, system integrations, ML,AI and data pipelines.

Responsibilities (QA Engineer – Data Science / ML)

• Establish QA best practices for Traditional ML and Generative AI workflows, including:
• Functional and regression testing of ML pipelines using pytest and Airflow/ Dagster test utilities and API testing tools (e.g., Postman, pytest-httpx ).
• Validate data contracts, schemas, and API compatibility across services using Pandera , and custom validation rules .
• Model behavior validation (input/output ranges, invariants, edge cases) using NumPy, SciPy, and statistical assertions
• Runtime and performance testing for inference latency, throughput, and resource usage using Locust, k6, or custom load tests .
• Integrate ML-specific tests into CI/CD pipelines using GitHub Actions, GitLab CI, or Jenkins, alongside containerized workflows (Docker, Kubernetes).
• Implement LLM-specific testing, including:
• Prompt and response validation, determinism checks, and regression testing using LangSmith .
• Evaluation of hallucinations, toxicity, and policy adherence using LLM-as-a-judge and /or rule-based checks .
• Cost, token usage, and timeout monitoring for GenAI workflows
• Verify logging, monitoring, and alerting for ML services using Prometheus, Grafana, and cloud-native observability tools.

Requirements

• BS or MS in Computer Science or a related field .
• 2-5 years of experience in Data or Machine Learning projects .
• Familiarity and experience of GenAI applications and tools - PyTorch , LangChain , vLLM etc.
• Demonstrates a commitment to continuous learning in this rapidly evolving field.
• Tools listed in the responsibilities section.