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# Machine Learning Engineer

- **Company:** MailerLite
- **Location:** Paris, France (Remote available)
- **Experience:** Experienced
- **Salary:** €55,000.0 - €80,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Big Data, Relational Databases, Distributed Computing Environment, Python (Programming Language), Machine Learning, Recommender Systems, SQL Databases, Reinforcement Learning, Feature Engineering, Pytorch, Delivery Pipeline, Large Language Models, Pandas, Scikit Learn, Xgboost
- **Published:** June 6, 2026
- **Apply:** https://fr.indeed.com/viewjob?jk=31820a5df385dde9

## About the Role

Do you have experience in SQL?, * 3+ years of experience building and shipping ML models in production (not just prototypes)

 * Strong applied ML fundamentals: feature engineering, calibration, leakage avoidance, and honest evaluation - especially on imbalanced and time-series problems
 * Hands-on LLM fine-tuning experience (supervised fine-tuning at minimum)
 * Fluency in Python and the modern ML stack (e.g. scikit-learn, gradient boosting, pandas/Polars, PyTorch)
 * Comfort writing performant SQL over large datasets and working with event/columnar stores and relational databases
 * Experience designing training and inference pipelines and the orchestration around them
 * A strong sense of ownership and the ability to work autonomously in a remote, async team
 * Clear written communication
 * At least 4 hours overlap required with CET time zone

Bonus skills

 * Preference optimization (DPO/RLHF-style) or reinforcement learning experience
 * Experience with multi-GPU / distributed training
 * Structured / constrained generation (function calling, schema-constrained output)
 * Experience building agentic, tool-using systems against real APIs
 * Anomaly detection, uplift/causal modeling, or recommender/segmentation work
 * Background in marketing, growth, deliverability, or other behavioral-data domains

## Description

* Build and ship predictive models on large-scale behavioral and event data - predicting engagement, finding the best time and audience for each message, scoring list health, and discovering customer segments
 * Fine-tune LLMs on our own data and outcomes to power a goal-driven assistant that recommends and takes action on a customer's behalf
 * Design and own the training and inference pipelines behind these models - data prep, training, evaluation, and serving
 * Build evaluation harnesses that prove a model is genuinely better before it ships - measuring real-world lift, not just offline metrics
 * Enforce reliable, structured model outputs so predictions and actions can be trusted in production
 * Collaborate with product and engineering teams who consume your models as shared infrastructure


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