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Data Engineer – Applied ML

Reads like a ML Engineer role: putting models into production.

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Employment
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What you’ll work on

Full posting
  • Design LLM and ML pipelines that classify, normalize and match product, brand and category data

  • Build agentic workflows with LangGraph or similar to automate data tasks end to end

  • Scale solutions to run over billions of records using Spark, Databricks and cloud infrastructure

  • Build evaluation frameworks with ground-truth datasets, labeling and quality monitoring

  • Take solutions from proof of concept to production and own them after launch

From the employer’s posting
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
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
Apply at Similarweb

Posting record

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Status in our records
Live
First seen by us
Oct 2, 202633 days ago
Recorded sightings
31
Last seen by us
Nov 4, 2026

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How this job compares

Time since we first saw it

Day 33

Similar jobs usually come down between day 6 and day 69.

From 4,339 similar postings that came down. Coming down is not always a hire.

Similarweb is hiring

15 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • Hiring at a steady pace
  • Takes jobs down at a usual pace
Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • AWS
  • Databricks
  • Spark
  • Hugging Face
  • PySpark
Where the posting names them
- 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 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
- 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
- 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

Your next step

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About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

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.

We help our customers succeed in today’s digital world by giving them access to data-driven insights, competitive benchmarks, strategic analysis, and more.

In 2021, we went public on the New York Stock Exchange, and we haven’t stopped growing since!

We’re looking for a Data Engineer with a strong applied ML focus to join our R&D department!

Why is this role so important at Similarweb?

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.

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.

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.

So, what will you be doing all day?

Your daily responsibilities may include:

  • 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

This is the perfect job for someone who:

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

 

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.

Why you’ll love being a Similarwebber:

You’ll actually love the product you work with: 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.

You’ll find a home for your big ideas: 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.

We offer competitive perks & benefits: 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.

You can grow your career in any direction you choose: Interested in becoming a VP or want to transition into a different department? Whether it’s Career Week, personalized coaching, or our ongoing learning solutions, you’ll find all the tools and opportunities you need to develop your career right here.

Diversity isn’t just a buzzword: People want to work in a place where they can be themselves. We strive to create a workplace that is reflective of the communities we serve, where everyone is empowered to bring their full, authentic selves to work. We are committed to inclusivity across race, gender, ethnicity, culture, sexual orientation, age, religion, spirituality, identity and experience. We believe our culture of equality and mutual respect also helps us better understand and serve our customers in a world that is becoming more global, more diverse, and more digital every day.

We will handle your application and information related to your application in accordance with the Applicant Privacy Policy available here: https://www.similarweb.com/corp/legal/applicant-privacy-policies/

We will handle your application and information related to your application in accordance with the Applicant Privacy Policy available here.

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Tel Aviv, Israel

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