# Lead Machine Learning Engineer – Recommendation Systems - Company: [Grai]() - Location: Warsaw, Poland - Remote: Yes - Team: R&D - Employment type: Full Time - Posted: July 1, 2026 ## Job description **GRAI** is a social music app out of Warsaw, currently in alpha. We’re building new ways to listen and respond through music - focusing on the social, human interactions that only happen when people are connected on both ends. To make those connections truly resonant, we’re looking for a **Lead Machine Learning Engineer** to architect, build, and scale our recommendation and discovery engines. You’ll own the technical roadmap for our RecSys stack, transforming raw user behavior and social signals into deeply personalized, real-time experiences. ## What You’ll Do * **Technical Leadership:** Define the long-term technical vision and architectural roadmap for our recommendation and personalization engines. * **System Architecture:** Design and oversee the implementation of scalable retrieval, ranking, and re-ranking pipelines capable of handling massive user behavior and content data. * **End-to-End Ownership:** Lead the development of robust ML infrastructure, including automated data processing, feature stores, MLOps, and real-time model monitoring. * **Mentorship & Culture:** Mentor and coach a talented team of ML engineers, fostering a culture of technical excellence, continuous learning, and rigorous experimentation. * **Data-Driven Strategy:** Design comprehensive offline evaluation frameworks and lead complex A/B testing strategies to validate and iterate on model performance. * **Cross-Functional Collaboration:** Partner closely with Product and Engineering to align ML initiatives with high-level business metrics and product goals. ## What We’re Looking For * **Proven Track Record:** Extensive experience designing, building, and scaling production-grade recommendation systems, search engines, or large-scale ranking models. * **Technical Mastery:** Deep, foundational knowledge of machine learning, deep learning architectures, and modern information retrieval methodologies. * **Scale & Infrastructure:** Significant experience with distributed data systems and modern ML frameworks (PyTorch, TensorFlow). Proven ability to handle massive, high-throughput user interaction datasets. * **Production & MLOps:** Strong background in deploying and maintaining low-latency models in production, with a solid grasp of feature stores, model registries, and drift monitoring. * **Leadership Skills:** Demonstrated experience leading technical projects, mentoring engineers, and successfully managing stakeholders without losing your hands-on technical edge. * **Pragmatic Execution:** Ability to balance cutting-edge AI experimentation with the practical realities of production stability, latency constraints, and business value. ## Nice to Have * **Real-Time Expertise:** Experience with streaming data architectures and real-time/session-based recommendation systems. * **Domain Knowledge:** Background in audio processing, music streaming, or high-growth consumer-facing personalization products. * **Advanced ML:** Familiarity with graph neural networks (GNNs), reinforcement learning, or leveraging Large Language Models (LLMs) for recommendation context. ## Why Join Us * **High Autonomy & Impact:** Direct ownership over the technical direction of high-impact ML systems used by millions of users. * **Founding-Stage Equity:** You’re joining a tight team at the alpha stage. We offer meaningful stock options so you have real skin in the game and share directly in the upside of what we build. * **Influence the Future:** Shape not just the recommendation stack, but the broader engineering culture and hiring roadmap of a fast-growing startup. * **Collaborative Innovation:** Work in a supportive, product-driven environment where your ideas directly dictate the future of how people experience music. * **Rest & Recharge:** High-output work requires real downtime. We offer 26 business days of paid time off per year, plus 5 days off and Polish public holidays. ## Apply [Apply on Grai]() Canonical job page: