Senior ML-Engineer

Highlights:

Location: Georgia

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Language: Fluent in Russian and English

We’re Fundraise Up - a global fundraising platform built to make donating to nonprofits fast, seamless, and accessible to all. Every month, our technology powers tens of millions of dollars in donations across the globe. We focus on innovation that directly impacts results: faster load times, higher conversion rates, global payment support, and accessibility-first design.

Our platform is trusted by many of the world’s leading nonprofits, including UNICEF, the Alzheimer’s Association, and a wide range of global NGOs. With a 4.9/5 rating across top software review platforms, we’re recognized not just for our impact - but for the quality of the product we deliver.

We operate in the enterprise segment, serving nonprofit organizations across North America, the United Kingdom, Australia, and Europe.

We’re building a large and complex product ecosystem that serves nonprofits, donors, and partners around the world. The platform includes a modern checkout experience and customizable widgets (each a standalone SPA), donor, organization, and partner portals, admin tools, and several internal apps.

We are a distributed team of 160+ product professionals. Our team members are mainly based across Spain, Poland, Portugal, Georgia, Armenia, Serbia, Turkey, and Cyprus.

Despite our scale, we operate like a focused team - where every task matters and every voice is heard. We value thoughtful collaboration, strong engineering practices, and a product mindset. You’ll be joining a team where quality, mentorship, and mutual respect come first.

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 — 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.

• 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.

• 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 Click House 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…