Machine Learning Engineer

About the role

Join Bynder AI Labs, a dedicated AI innovation team responsible for researching, prototyping, and shipping the next generation of AI-powered capabilities for Bynder's platform. As a Machine Learning Engineer, you will design, build, and deploy AI-powered features, architect and implement solutions using AWS AI services, and collaborate with product stakeholders and end-users. Enjoy a hybrid work environment, comprehensive health plans, unlimited holidays, and more. Key missions: Design, build, and deploy AI-powered features for Bynder’s DAM platform, focusing on content discoverability, automation, and intelligent workflows.. Architect and implement solutions using AWS AI services (Bedrock, SageMaker, Lambda) and large language model APIs.. Collaborate directly with product stakeholders and end-users to validate AI prototypes and gather feedback for successful delivery. Profile: - Familiarity with agentic AI frameworks (e.g., LangChain, LangGraph, CrewAI, or similar) and understanding of agent architectures including planning, memory, and tool use - Solid foundation in mathematics: linear algebra, probability, statistics, and optimization. Understanding of classical ML algorithms and when they apply - Strong software engineering fundamentals: Python, REST APIs, Git, Docker, and CI/CD - Bachelor’s degree in Computer Science, AI, Mathematics, Physics, or a related field - Practical experience building systems with embeddings and vector databases for semantic search or retrieval-augmented generation (RAG) - Minimum of 3+ years of experience in Machine Learning Engineering, AI Engineering, Data Science, or a similar role - Strong understanding of how large language models work: transformer architectures, attention mechanisms, tokenization, embeddings, and inference pipelines - Hands-on experience with cloud AI services, particularly AWS (Bedrock, SageMaker, Lambda) - Master’s degree or PhD in a relevant field - Experience with prompt engineering, context engineering, and LLM evaluation frameworks - Experience working in a B2B SaaS or enterprise software environment - Familiarity with GDPR and data privacy considerations for AI systems in Europe - Experience with computer vision or image/video processing (highly relevant to our DAM domain) - Knowledge of MLOps practices: model monitoring, A/B testing, and deployment pipelines for AI features - Proficiency with AI-assisted coding tools and workflows (GitHub Copilot, Cursor, Claude Code, etc.) - A growth mindset and the determination to improve yourself and others - Great communication skills in English. You can translate complex AI concepts for non-technical stakeholders - A responsible approach to AI, thinking about bias, fairness, transparency, and the real-world impact of the systems you build - A self-starter mentality. You thrive in an R&D environment with ambiguity and open-ended challenges - Intellectual curiosity and a genuine passion for AI, with a habit of staying current with the latest developments - The ability to connect the dots between mathematical theory, engineering implementation, and product value