<p>Similarweb is the leading digital intelligence platform used by over 3500 global customers. Our wide range of solutions power the digital strategies of companies like Google, eBay, and Adidas.</p> <p>We help our customers succeed in today’s digital world by giving them access to data-driven insights, competitive benchmarks, strategic analysis, and more.</p> <p>In 2021, we went public on the New York Stock Exchange, and we haven’t stopped growing since!</p> <p>We’re looking for a Data Engineer with a strong applied ML focus to join our R&D department!</p> <h2><span style="font-size: 12pt;"><strong>Why is this role so important at Similarweb?</strong></span></h2> <p>Our Retail Intelligence products help leading brands and retailers understand how their products, brands and categories perform online. Behind them is data collected from retailers and marketplaces around the world: product pages, brands and categories, each described differently by every site.</p> <p>Your mission is to turn that data into a single, trusted view: classifying products into a unified taxonomy, normalizing brands and attributes, and matching the same entities across sources. And doing it at scale, across a catalog of more than a billion records that keeps growing and changing every day.</p> <p>This is an applied ML role within data engineering. You’ll build with LLMs, agentic frameworks such as LangGraph, embeddings and classical ML, and ship them as production pipelines. It’s hands-on work, not research for its own sake, but it takes a real understanding of classification and NLP methods to choose the right tool for each problem and prove that it works.</p> <h2><span style="font-size: 12pt;"><strong>So, what will you be doing all day?</strong></span></h2> <p>Your daily responsibilities may include:</p> <ul> <li>Designing and building LLM-powered and ML-based pipelines that classify, normalize, structure and match product, brand and category data</li> <li>Building agentic workflows (LangGraph or similar) that automate complex data tasks end to end</li> <li>Choosing the right approach for each problem (LLMs, embeddings, fine-tuned models, classical classifiers or rules), balancing accuracy, cost and latency</li> <li>Scaling solutions to run efficiently over billions of records, using Spark, Databricks and our cloud infrastructure</li> <li>Building evaluation frameworks: ground-truth datasets, labeling processes, quality metrics and ongoing monitoring</li> <li>Taking solutions from POC to production, and owning them after launch</li> <li>Working closely with Product to define requirements and shape the roadmap</li> <li>Collaborating with data engineers, data scientists and other R&D teams on infrastructure and best practices</li> </ul> <h2><span style="font-size: 12pt;"><strong>This is the perfect job for someone who:</strong></span></h2> <ol> <li>Holds a B.Sc. or M.Sc. in Computer Science, Data Science, Mathematics or another relevant field</li> <li>Has 4+ years of hands-on experience as a data engineer, ML engineer or data scientist, with solutions running in production</li> <li>Has strong Python skills and writes production-quality code</li> <li>Has hands-on experience building LLM-based applications in production (prompt engineering, structured outputs, RAG, embeddings, evaluation)</li> <li>Has worked with the modern LLM stack: LLM provider APIs (OpenAI, Anthropic, etc.), LangGraph or LangChain, Hugging Face and vector stores</li> <li>Has a solid grasp of text classification and NLP methods, both classical and modern, and knows when to use each</li> <li>Has experience processing large-scale data with Spark/PySpark, Databricks or similar, on AWS or another cloud</li> <li>Understands evaluation and data quality well: precision/recall trade-offs, building ground truth, error analysis</li> <li>Is pragmatic and delivery-focused, comfortable with ambiguity, and communicates clearly with Product and business stakeholders</li> <li>Has experience with taxonomies, entity resolution or product/e-commerce data (advantage)</li> <li>Has experience with fine-tuning or deploying open-source models (advantage)</li> </ol> <p> </p> <p><strong>At Similarweb, collaborating with our colleagues in-office creates a more connected, unified culture. Our best work is a product of our face-to-face collaboration, with the ability to work partially from home.</strong></p> <h2><span style="font-size: 12pt;"><strong>Why you’ll love being a Similarwebber:</strong></span></h2> <p><strong>You’ll actually love the product you work with: </strong>Our customers aren’t our only raving fans. When we asked our employees why they chose to come work at Similarweb, 99% of them said “the product.” Imagine how exciting your job is when you get to work with the most powerful digital intelligence platform in the world.</p> <p><strong>You’ll find a home for your big ideas: </strong>We encourage an open dialogue and empower employees to bring their ideas to the table. You’ll find the resources you need to take initiative and create meaningful change within the organization.</p> <p><strong>We offer competitive perks & benefits: </strong>We take your well-being seriously, and offer competitive compensation packages to all employees. We also put a strong emphasis on community, with regular team outings and happy hours.</p> <p><strong>You can gr