QA Engineer(AI/ML focus) - Remote D886

Argentina
Permanente
Tiempo completo

Hace 12 horas

About UsAt Zyte, we eat data for breakfast and you can eat your breakfast anywhere and work for Zyte. Founded in 2010, we are a globally distributed team of over 250 Zytans working from over 28 countries who are on a mission to enable our customers to extract the data they need to continue to innovate and grow their businesses. We believe that all businesses deserve a smooth pathway to dataFor more than a decade, Zyte has led the way in building powerful, easy-to-use tools to collect, format, and deliver web data, quickly, dependably, and at scale. And today, the data we extract helps thousands of organizations make smarter business decisions, secure competitive advantage, and drive sustainable growth. Today, over 3,000 companies and 1 million developers rely on our tools and services to get the data they need from the web.Data QA is an important function within Zyte. The Data QA team works to ensure that the quality and usability of the data scraped by our web scrapers meets and exceeds the expectations of our enterprise clients.Are you passionate about data and data quality and integrity?Do you enjoy using Python and AI to analyze and manipulate data, detect data quality issues, and visualize your findings?Are you highly customer-focused with excellent attention to detail?Owing to growing business and the need for ever more sophisticated Data QA, we are looking for a talented Data Scientist to join our team. As a Zyte Engineer, you work on AI-based data wrangling, data manipulation, and data visualisation techniques and apply them in the verification and validation of data quality as it pertains to data extracted from the web.RequirementsRoles & Responsibilities:- Design and implement AI-driven quality checks: build models to detect anomalies, identify schema drift, and classify data errors in real time.
- Automate and scale QA: replace manual and rule-based validation with ML-powered solutions that continuously improve.
- Leverage GenAI for validation: use embedding models, LLMs, and prompt-driven pipelines to perform semantic checks on scraped data.
- Develop monitoring & alerting pipelines: quantify data quality via KPIs, dashboards, and automated reports for stakeholders.
- Experiment & innovate: research and prototype new AI techniques for QA, using embeddings, synthetic data, and reinforcement learning to stress-test scrapers.
- Collaborate cross-functionally: work with developers, product managers, and account teams to integrate AI-based QA into production workflows.
- Communicate insights: present findings with clear visualizations, metrics, and evidence-based recommendations to technical and non-technical audiences.Requirements:- Proficiency in Python & PyData stack (NumPy, pandas, scikit-learn, PyTorch/TensorFlow preferred).
- 3+ years in a data science, applied ML, or data engineering role (ideally with exposure to QA or data validation at scale).
- Hands-on experience with GenAI tools: LLM APIs (OpenAI, Anthropic, Google), prompt engineering, cost/token optimization.
- Strong ML fundamentals: anomaly detection, classification, clustering, embeddings, evaluation metrics.
- Experience with big data frameworks (Spark, BigQuery, or similar).
- Ability to work with very large datasets (millions+ of records).
- Version control skills (GitHub/Bitbucket).
- Excellent communication in English, both technical and non-technical.Desired Skills:- Prior experience in data quality automation or web data QA.
- Familiarity with LangChain, MCP, Marvin, or similar orchestration frameworks.
- Experience building QA dashboards or visualization layers.
- Background in statistics or applied mathematics.
- Previous remote/distributed work experience.BenefitsAs a new Zytan, you will:Become part of a self-motivated, progressive, multi-cultural team.Have the freedom and flexibility to work from where you do your best work.Attend conferences and meet with team members from across the globe.Work with cutting-edge open source technologies and tools.

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