חזרה

Data Engineer – Applied ML

Similarweb
שכר לא צויןTel Aviv, Israel, היברידיסניורמשרה מלאה
משרה חיצונית, ההגשה באתר החברהאושר שהמשרה פתוחה לפני 9 שעות

Build production pipelines that classify, normalize, structure, and match product, brand, and category data at scale. Apply LLMs, embeddings, and classical ML to create a trusted view of data from retailers and marketplaces.

מה תעשו

  • Build LLM-powered and ML-based pipelines for product, brand, and category data.
  • Create agentic workflows using LangGraph or similar frameworks.
  • Scale data solutions across billions of records using Spark, Databricks, and cloud infrastructure.
  • Build evaluation frameworks with ground-truth datasets, quality metrics, and monitoring.
  • Take solutions from proof of concept to production and own them after launch.
  • Work with Product and R&D teams to define requirements and improve infrastructure.

דרישות

  • B.Sc. or M.Sc. in Computer Science, Data Science, Mathematics, or a relevant field.
  • 4+ years of hands-on experience as a data engineer, ML engineer, or data scientist, with production solutions.
  • Strong Python skills and ability to write production-quality code.
  • Hands-on experience building production LLM applications, including prompt engineering, structured outputs, RAG, embeddings, and evaluation.
  • Experience processing large-scale data with Spark/PySpark, Databricks, or similar on AWS or another cloud.

יתרון

  • Experience with taxonomies, entity resolution, or product/e-commerce data.
  • Experience fine-tuning or deploying open-source models.

תנאי סף

  • B.Sc. or M.Sc. in Computer Science, Data Science, Mathematics, or a relevant field
  • 4+ years of hands-on experience as a data engineer, ML engineer, or data scientist
  • Strong Python skills
  • Experience building production LLM applications

הטבות

  • עבודה היברידית עם אפשרות לעבוד חלקית מהבית.
  • תקציב והזדמנויות ללמידה ולהתפתחות מקצועית.
  • פעילויות צוות ומפגשים חברתיים.
PythonLLMsLangGraphLangChainNLPSparkDatabricksEmbeddings