Senior ML Engineer

We’re looking for an 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 (generation, classification, agents). 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 LLM-powered features: from simple classifiers and content generation to complex AI assistants and chatbots.

• Work across the full LLM lifecycle: golden datasets, prompt engineering, fine-tuning, and response 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) and understanding of modern NLP/LLM approaches

• 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

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

Bonus points

• Curiosity and a hypothesis-driven mindset

• Ability to communicate complex analytical concepts to non-technical audiences

• Detail-oriented with a strong sense of ownership

• Comfort working in fast-paced, data-rich environments

Why work with us

• A strong, collaborative product team that owns what it builds

• Clear product vision and access to real customer feedback from global nonprofit leaders

• Flat structure: no politics, just great work with great people

• Transparent company culture-we share how we’re growing, where revenue comes from, and what’s next

• Long-term focus: we offer equity options and value sustained, meaningful contribution

Benefits

• 31 days off

• 100% paid telemedicine plan

• Home Office Setup Assistance: the company offers assistance with purchasing furniture (office chair, office desk, monitor) and other items to create a comfortable workspace.

• English learning courses

• Relevant professional education

• Gym or swimming pool

• Co-working

• Remote working