Описание

Similarweb, a leading digital intelligence platform, is seeking a Data Engineer with a strong applied ML focus to join its R&D department.

Designing and building LLM-powered and ML-based pipelines that classify, normalize, structure and match product, brand and category data
Building agentic workflows (LangGraph or similar) that automate complex data tasks end to end
Choosing the right approach for each problem (LLMs, embeddings, fine-tuned models, classical classifiers or rules), balancing accuracy, cost and latency
Scaling solutions to run efficiently over billions of records, using Spark, Databricks and our cloud infrastructure
Building evaluation frameworks: ground-truth datasets, labeling processes, quality metrics and ongoing monitoring
Taking solutions from POC to production, and owning them after launch
Working closely with Product to define requirements and shape the roadmap
Collaborating with data engineers, data scientists and other R&D teams on infrastructure and best practices

Holds a B.Sc. or M.Sc. in Computer Science, Data Science, Mathematics or another relevant field
Has 4+ years of hands-on experience as a data engineer, ML engineer or data scientist, with solutions running in production
Has strong Python skills and writes production-quality code
Has hands-on experience building LLM-based applications in production (prompt engineering, structured outputs, RAG, embeddings, evaluation)
Has worked with the modern LLM stack: LLM provider APIs (OpenAI, Anthropic, etc.), LangGraph or LangChain, Hugging Face and vector stores
Has a solid grasp of text classification and NLP methods, both classical and modern, and knows when to use each
Has experience processing large-scale data with Spark/PySpark, Databricks or similar, on AWS or another cloud
Understands evaluation and data quality well: precision/recall trade-offs, building ground truth, error analysis
Is pragmatic and delivery-focused, comfortable with ambiguity, and communicates clearly with Product and business stakeholders

Collaborating with colleagues in-office creates a more connected, unified culture
Ability to work partially from home
Competitive compensation packages
Emphasis on community, with regular team outings and happy hours
Opportunities for career growth and development, including Career Week, personalized coaching, and ongoing learning solutions

Data Engineer – Applied ML