Fundraise Up

Fundraise Up

Fundraise Up specializes in enhancing online donation processes through AI-driven conversion optimization and integrated payment solutions, enabling organizations to maximize their fundraising potential and improve donor engagement.

Capital Markets
51-250
Founded 2017

Description

  • Develop and deploy ML solutions across business areas using tabular data, uplift models, and recommender systems.
  • Select appropriate ML, LLM, or alternative approaches for each problem.
  • Build end-to-end ML systems, including data preparation, training, API development, and monitoring.
  • Design LLM-powered features such as classifiers, content generation tools, AI assistants, and chatbots.
  • Work across the full LLM lifecycle, including dataset creation, prompt engineering, fine-tuning, and response evaluation.
  • Collaborate with product and business stakeholders on experiments and solution design for multiple product teams.
  • Optimize donation amounts, upsell offers, and other product outcomes through applied machine learning.
  • Support classification, transaction analysis, and other product-facing ML initiatives.

Requirements

  • 5+ years of ML/DS experience solving real product problems.
  • Strong expertise in machine learning and mathematical statistics, including classical algorithms such as gradient boosting and modern NLP/LLM approaches.
  • Metrics-driven mindset with the ability to connect ML metrics such as ROC-AUC, F1, and RMSE to business metrics such as CR and LTV.
  • Strong Python engineering skills with a product-oriented approach, clean code practices, design patterns, and solid engineering habits.
  • Advanced SQL skills and the ability to build complex datasets in ClickHouse and work with MongoDB.
  • Hands-on MLOps experience with experiment tracking tools and production workflows such as Docker, Git, and CI/CD.
  • Ability to break down problems, choose the right tech stack, justify non-ML solutions when appropriate, and deliver to production.
  • Strong English required at B2 level; the team primarily communicates in Russian.
  • Experience with classical ML, RL, and LLM-based solutions, including generation, classification, and agents, is expected.
  • Familiarity with the listed tech stack, including FastAPI, Pydantic, CatBoost, CausalML, OpenAI, MLflow, Airflow, Grafana, Sentry, pandas, Polars, and Redis.

Benefits

  • Private medical insurance for the employee and their family.
  • 23 paid vacation days per year plus 11 paid public holidays.
  • 5 company-paid sick leave days.
  • English learning courses and relevant professional education support.
  • Gym or swimming pool benefit.
  • Home office setup assistance for furniture and equipment.
  • Co-working support and remote working.
  • Equity options and a long-term focus on meaningful contribution.

Interested in this position?

Apply directly on the company website

Apply Now

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