Senior Machine Learning Engineer

ManoMano

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

Posted 9/19/2026contractParis • FranceSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing machine learning and AI solutions, with a focus on product categorization, attribute extraction, and model evaluation. Proficient in developing scalable pipelines and ensuring the reliability and performance of AI systems in production environments.

Highest-signal resume keywords

Machine Learning EngineeringDeep LearningPython ProgrammingModel ExperimentationData Pipeline Design

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningDeep LearningAI EngineeringModel EvaluationProduct CategorizationAttribute ExtractionEntity ResolutionSemantic SimilaritySQLScalable Processing Frameworks

Soft Skills

Analytical SkillsUser-Focused MindsetCommunication SkillsGrowth Mindset

Tools & Technologies

MLOps PracticesExperimentation ToolsDaskRaySpark

Industry Keywords

E-CommerceB2C MarketplaceRecommendation SystemsPersonalization

Tech Stack

Tools & technologies

PythonRaySparkSQL

About the role

Key responsibilities & impact

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
Design pragmatic human-in-the-loop and automated workflows combining machine learning, AI models, rules, and internal data sources
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 offline benchmarks, run experiments, and communicate results
Partner with software engineers, product managers, and business stakeholders to frame problems from scientific and business perspectives
Investigate and fix production issues; ensure reliability, observability, and performance of AI systems
Stay engaged with developments in machine learning, Generative AI, information extraction, entity resolution, and scalable ML systems

Requirements

What you’ll need

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
Experience in e-commerce or B2C marketplace environments is nice to have
Familiarity with experimentation tools and MLOps practices is nice to have
Experience with scalable processing frameworks (Dask, Ray, Spark, etc.) is nice to have
Some familiarity with Bayesian inference and causal inference is nice to have
Knowledge of recommendation systems and personalization is nice to have

Benefits

Comp & perks

Part-time remote option (max 2 days per week)
Flexible working hours
Health care coverage
Meal Voucher: Swile Card
Employee discount on our DIY & HI offering
Take care of your mental health with our dedicated partner with moka.care
Free access to a gym in Paris