Senior Machine Learning Engineer - M/W ManoMano

In less than a decade, ManoMano has become a key player in the home improvement and renovation sector.

Launched in 2013, ManoMano is the reference online marketplace for DIY, home improvement and gardening. Co-founded by Philippe de Chanville and Christian Raisson, ManoMano brings together the largest offer of DIY & gardening online products: electricity, plumbing, hardware, frames, indoor and outdoor furniture, tools, etc. With more than 5 000 seller partners and 11 million products, ManoMano currently employs 500 people and operates in 6 markets (France, Belgium, Spain, Italy, Germany, United Kingdom).

Motivated by the prospect of improving the living environment of their customers and convinced of the importance of the home market for sustainable consumption habits, the ManoMano teams want to help write a new page in their industry, which is struggling to reform itself. ManoMano brings to a highly technical world the power of its sector expertise, combined with that of data and digital in all its dimensions, to offer our customers easy access to innovative advice, products and services 100% online.

The ambition of the Founders and, above all, of Manas & Manos? To accompany this sector transformation with a strong culture of boldness, in an ingenious and frugal organization that places people and teams at the heart of the company's development.

OUR COMPANY CULTURE

People are at the heart of ManoMano's culture around our 3 core values: boldness, ingenuity, and responsibility.

TEAM AND CONTEXT

The Machine Learning team is an applied ML team with a strong focus on delivery. We are outcome-oriented: we solve high-impact business problems and strive to deliver value to our customers.

We are seeking a Senior Machine Learning Engineer for our Paris office to join the Machine Learning team and improve the quality of ManoMano's product catalog. You will work on high-impact challenges including product categorization, product qualification and attribute extraction, product matching, and catalog enrichment using machine learning and AI. You will help design and productionize robust solutions that make millions of products easier to discover, compare, and use.

The ML team is instrumental in the growth of ManoMano and is now fully committed to building AI and ML products on our marketplace. You will be at the forefront of that transformation, designing, deploying, and iterating on AI solutions at scale.

If you wish to know more about what we do:

• How do we forecast delivery times at MM

• How do we leverage LLMs for attribute extraction

• Our take on how to tackle position bias
  

YOUR RESPONSIBILITIES

• Design and implement machine learning and AI solutions for catalog quality, including product categorization, qualification, attribute extraction, product matching, and enrichment.

• Build and maintain scalable pipelines for product classification, entity matching, semantic similarity, and attribute extraction across a large and continuously evolving product catalog.

• Develop, adapt, and evaluate machine learning models, including LLMs and vision-language models, for domain-specific catalog tasks such as categorization, attribute extraction, product matching, and content enrichment.

• Design pragmatic human-in-the-loop and automated workflows that combine machine learning, AI models, rules, and internal data sources to improve catalog quality.

• Write production-ready code and deploy AI systems in a live environment at scale.

• Define and track evaluation metrics for catalog quality and model performance; create reliable offline benchmarks, run experiments, and communicate results clearly to guide technical and product decisions.

• Partner with software engineers, product managers, and business stakeholders to frame problems from both a scientific and business perspective.

• Investigate and fix production issues; ensure reliability, observability, and performance of AI systems.

• Stay actively engaged in technology watch on the latest developments in machine learning, Generative AI, information extraction, entity resolution, and scalable ML systems.
  

Technical stack:

• Python

• AWS

• Airflow

• Kubernetes

• Gitlab

• Snowflake

• Vector databases (e.g. PGVector)

• LLM inference & fine tuning frameworks (OpenAI SDK, vLLM, unsloth, or similar)

MUST HAVE

• User-focused mindset with strong analytical skills and a result-oriented approach.

• More than 5 years of experience in Machine Learning, Deep Learning, or AI Engineering, including taking models from prototype to production at scale.

• Hands-on experience developing and evaluating machine learning or AI solutions for real-world data, with strong experience in model experimentation, evaluation, and benchmarking. Experience with LLMs or VLMs fine tuning is a plus.

• Strong experience with at least some of the following: product categorization, taxonomy design, attribute extraction, entity resolution, product matching, semantic similarity, embeddings, or information retrieval.

• Experience designing robust data and machine learning pipelines for large-scale production use cases.

• Strong scientific rigor and ability to design metrics aligned with catalog quality and product goals, run experiments, analyze errors, and communicate results to guide technical and product decisions.

• Experience with large-scale applications in production (monitoring, reliability, performance, observability).

• Strong coding skills in Python and proficiency in SQL. You care about code simplicity and performance.

• Proficient oral and written communication skills in English.

• Growth mindset: always striving to improve your technical and soft skills.

NICE TO HAVE

• Experience in e-commerce or B2C marketplace environments.

• Familiarity with experimentation tools and MLOps practices.

• Experience with scalable processing frameworks (Dask, Ray, Spark, etc.).

• Some familiarity with Bayesian inference and causal inference.

• Knowledge of recommendation systems and personalization.

HIRING PROCESS

• Introductory call with a talent acquisition manager to get a feel for your motivations and talk about the role.

• A meeting with a Data Scientist Manager (30min).

• A take-home technical assignment to assess your machine learning, data processing, and programming skills (3 to 4 hours for an experienced Senior Machine Learning Engineer).

• On-site or virtual technical interview with a Senior and a Lead ML/AI Engineer (2h): discuss your take-home assignment and go over internal AI use cases; we assess your critical thinking, knowledge of AI systems, and pragmatism.
  
  Apply now and join an exciting adventure ! ✨
  
  You are welcome to apply to ManoMano, regardless of your gender, religion, age, sexual orientation, ethnicity, disability.