Job description
MailerLite is one of the fastest-growing email marketing services. We help more than 1 million businesses around the world stay in touch with their customers. Today, we are a team of more than 170 dreamers, adventurers, and world travelers passionate about what we do and what we believe in. And we are ready for another talented person to join the party.
We’re looking for a Machine Learning Engineer to help us build the intelligence layer behind our products - turning the behavioral data of over a million businesses into predictions and actions that help our customers reach the right people, at the right time, on the right channel, without needing a full marketing team. You’ll work across the full ML spectrum: classical predictive models on large-scale behavioral data and applied LLM work, including fine-tuning models on our own data to power a goal-driven assistant.
We’re betting the next phase of MailerLite on ML, and this is one of the first hires to make it real. The data’s already here. The team is still taking shape — and you’ll play a key role in defining it. We're looking for someone who's excited to help build what's next. 🚀
Why MailerLite?
You'll build ML that actually ships to customers
This isn't a research role that produces slide decks. You'll own models end to end — from data and training pipelines to production — and see them drive real business outcomes fast. You'll work on predictive models that surface audience insights and LLM-based assistants that turn those signals into actionable recommendations.
You'll grow, develop and evolve
As part of a team that’s always looking for new, innovative ways to create value for our customers, you'll constantly be experimenting, learning, and trying out new approaches. You won’t just execute tasks — you'll be encouraged to question assumptions, explore better solutions, and help shape how we use ML across the product over time.
You'll take ownership
We expect you to take full responsibility and ownership of your tasks. Team leads avoid micromanaging and minimize interruptions so you can stay focused on your assignments.
You’ll have experts on hand
Whenever you’re stuck, your teammates with a wide range of expertise are ready to help you grow. And they’d love for you to share your knowledge too!
You'll pick where you work, every day
We embrace the remote culture. Every day you get to choose the environment makes you most productive.
You'll have stability
We value a stable workplace! MailerLite has been thriving for over 10 years and our year-over-year growth continues to increase.
What you’ll work on
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
Job requirements
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
