Senior ML-Engineer

About the Role

We're looking for a Senior ML Engineer with 5+ years of production experience to strengthen our ML team. We operate as an internal service and centre of excellence for 10+ product teams at Fundraise Up. This means you won't be tied to a single feature: one day you might be optimising donation amounts and upsell offers, and the next you could be building a smart assistant for the admin panel or working on transaction classification.

We actively use not only classical ML, but also RL, and we're expanding our LLM-based solutions — prompt engineering and pipeline design with LLM APIs (OpenAI and equivalents). That's why we're looking for someone with a broad mindset who isn't afraid to experiment and can choose the most effective approach for each task.

The project's main audience and business team are based in the US. Although the product development team is Russian-speaking, you may occasionally need to write in English.

What You’ll Do

Develop and deploy ML solutions across different business areas using tabular data (uplift models, recommender systems). We are also actively developing projects that will involve e-commerce mechanics.

Select the most appropriate ML/LLM approaches or propose alternative solutions.

Build end-to-end ML solutions: data preparation, training, API development, and monitoring.

Design prompts and LLM API-based pipelines for specific product tasks: classification, content generation, and response quality evaluation.

Requirements

5+ years of ML/DS experience solving real product problems

Strong expertise in ML and mathematical statistics: solid knowledge of classical algorithms (especially gradient boosting); NLP knowledge is a plus

Metrics-driven mindset: ability to connect ML metrics (ROC-AUC, F1, RMSE) with business metrics (CR, LTV)

Strong engineering culture: confident in Python with a product-oriented approach to development; we value clean code, knowledge of design patterns, and solid engineering practices

Data skills: advanced SQL; ability to independently and efficiently build complex datasets in ClickHouse and work with MongoDB

MLOps understanding: hands-on experience with experiment tracking tools and understanding of production workflows (Docker, Git, CI/CD)

Autonomy: ability to break down problems, choose the right tech stack (or justify a non-ML solution), and deliver to production

At Fundraise Up, AI is a default tool, not an experimental one. We expect every team member to actively use AI in their day-to-day work, identify where AI can change the shape of problems in their function, and grow their fluency as the tools evolve. You should already be using AI meaningfully in your work and understand where it adds value and how it can improve the way you operate

Our Tech Stack

Core: Python (uv, ruff), FastAPI, Pydantic, Docker

Models: CatBoost, Uplift Modeling (CausalML), OpenAI (RAG, Prompt-Engineering)

Data: ClickHouse, MongoDB, pandas, Polars, Redis

MLOps: MLflow, Airflow

Monitoring: Grafana, Sentry