Lead Machine Learning Engineer

Join our team as a Lead Machine Learning Engineer, where you will head up our ML & AI team and the platform behind it. You will own the architecture of our ML & AI systems, design and build our recommendation, search, and ranking systems, and deploy models as robust APIs and scalable microservices. You will also manage business stakeholders, align priorities, and lead, mentor, and grow a team of ML engineers. The ideal candidate has experience in building AI agents and LLM-based products in production, a strong grounding in experimentation and statistics, and expertise in Python and PySpark.

• Lead the architecture and development of machine learning and AI systems, including data pipelines, feature and model stores, and training and serving infrastructure.
• Design and build recommendation, search, and ranking systems end to end, including candidate generation, retrieval, ranking models, and real-time personalization.
• Manage and mentor a team of machine learning engineers, supporting their career development and ensuring chapter growth.

• 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
• 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
• Master’s in Computer Science, Statistics, Mathematics, or a related field
• 7+ years in ML engineering, including 2 years leading technical direction of teams (formal people management is a plus)
• A track record of building and operating ML/AI systems in production, across the full lifecycle: feature engineering, model development, deployment and monitoring
• Strong grounding in experimentation and statistics: metric design, A/B testing and causal inference to quantify business impact
• 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
• Expertise in Python and PySpark, with a solid grasp of ML algorithms and frameworks (MLlib, Scikit-learn, TensorFlow/PyTorch, MLflow). Familiarity with a language such as Java or Kotlin is a plus
• Experience with recommender systems at scale: retrieval and candidate generation, ranking models, and real-time personalization in high-load production environments