Machine learning engineer for auction platforms

At TBAuctions, we are building the intelligent auction platform behind our brands, with machine learning and AI at its core: powering recommendations, search and personalization, valuation, supply-demand matching and streamlining processes with AI agents across millions of auctions. We are looking for a Lead Machine Learning Engineer to head up our ML & AI team and the platform behind it, in a role that is equal parts hands-on and leadership. The ideal candidate has led and delivered large-scale ML/AI projects to production with real business impact, shaped technical direction, and built strong teams.

• Own the architecture of our ML & AI systems end to end: data pipelines, feature and model stores, training and serving infrastructure, built as reusable capabilities our auction brands can share.
• Design the architecture and orchestration of our AI agents platform, with strong evaluation, monitoring and guardrails for reliable behaviour in production.
• Strengthen and contribute to our existing MLOps setup: automated training, validation, deployment and monitoring, experiment tracking and CI/CD.
• Design and build our recommendation, search and ranking systems end to end: candidate generation and retrieval, ranking models, embeddings and vector search, and real-time personalization at scale.
• Deploy models as robust APIs and scalable microservices, containerized and orchestrated with Kubernetes.
• Design systems, write and review code in a modern stack, and raise the bar on engineering best practices.
• Identify and prioritize the highest-impact opportunities across our auction brands and landscape, set and own the ML & AI roadmap.
• Frame business problems as ML tasks, define most impactful business metrics, and run rigorous experiments (A/B tests) to measure real impact.
• Drive continuous, incremental improvement of our ML & AI products, balancing near-term delivery with longer-term investment in the platform and innovation.
• Work closely with product managers and other tech leads to align priorities and delivery across teams.
• Manage business stakeholders and align them on priorities, scope and trade-offs, translating complex technical concepts to non-technical audiences effectively.
• Lead, mentor, hire and grow a team of ML engineers, supporting their career development and upskilling, and ensure chapter growth.

• 7+ years in ML engineering, including 2 years leading technical direction of teams (formal people management is a plus).
• Master’s in Computer Science, Statistics, Mathematics, or a related field.
• Experience with recommender systems at scale: retrieval and candidate generation, ranking models, and real-time personalization in high-load production environments.
• A track record of building and operating ML/AI systems in production, across the full lifecycle: feature engineering, model development, deployment and monitoring.
• Strong systems, architecture and MLOps background: distributed, event-driven ML systems, API and model serving as microservices, containerization and orchestration with Kubernetes / AKS, and CI/CD and experiment tracking.
• Strong grounding in experimentation and statistics: metric design, A/B testing and causal inference to quantify business impact.
• Expertise in Python and PySpark, with a solid grasp of ML algorithms and frameworks (MLlib, Scikit-learn, TensorFlow/PyTorch, MLflow).
• Experience in building AI agents and LLM-based products in production, including memory, context engineering, retrieval and orchestration, and multimodal retrieval systems/architecture.
• Comfortable splitting your time between building directly and leading, and happy to stay hands-on in the code and architecture.
• Excellent communication skills, able to explain complex technical choices and trade-offs to non-technical stakeholders.
• Будет плюсом: Familiarity with a language such as Java or Kotlin is a plus; formal people management is a plus

• A hands-on leadership role with real ownership, within an entrepreneurial and innovative organization
• A modern data stack (FastAPI, Databricks, Airflow, DBT) and cloud-native deployment on Azure with Kubernetes (AKS).
• Independent and varied role within an entrepreneurial and innovative organization
• Gross yearly salary of € 100.000 - € 120.000 (including holiday allowance), depending on experience
• Bonus scheme
• Pension scheme
• 25 vacation days
• Laptop and iPhone
• Training opportunities
• Working in a team of professionals, where hard work is well combined with humor