About the role
HoneyBook is the leading AI-powered business management platform for service-based business owners. Designed to enhance - not replace - independent professionals. HoneyBook’s AI-powered tools help businesses attract leads, connect with clients, book projects, and manage payments more efficiently. With AI seamlessly integrated into every workflow, entrepreneurs can focus on their craft while scaling their businesses with confidence. Since its founding in 2013, HoneyBook has powered over 25 million client relationships and processed more than $12 billion in transactions, helping independent businesses grow faster and smarter. Our culture is built on five core values that inform everything we do. We encourage collaboration, feedback, ownership, and maintain a growth mindset. We know experience comes in many forms - some visible on your resume, others not. No one candidate will be a 100% perfect match to our description, so if you thrive in a fast-paced, intellectually curious environment and have relevant experience, we encourage you to apply. We're looking for a ML Engineer to join our Data team in Tel Aviv. Our team sits at the heart of HoneyBook's data stack - we build and maintain the infrastructure, pipelines, and AI-powered systems that DS, product, marketing and other stakeholders rely on to move fast and make smart decisions.
What you'll work on: Integrate and productionize LLM-based systems: from prompt registries and model gateways to agentic workflows - with a focus on reliability, observability, and cost efficiency. Own our core MLOps infrastructure: build and maintain our feature store, vector database, training and monitoring tools, etc.- so DS and product teams can ship AI features without infrastructure becoming the bottleneck. Build AI-powered Python microservices that integrate LLMs, scraping, pipelines and internal data into product features used by hundreds of thousands of businesses. Collaborate deeply with data scientists, engineers and product managers to turn ideas into reliable, scalable ML systems, iterating quickly from prototype to production. Own initiatives end-to-end: from design doc to production deployment, including monitoring, documentation, and handoff. Here is what is needed: 4+ years of production engineering experience, with at least 3 years working on data-intensive systems in Python. Mindset of ownership, curiosity, and can-do attitude in a fast‑moving environment. Hands-on experience with LLM-based systems in production - prompt management, scale, cost optimization, latency, and reliability. Distributed systems fluency - event-driven architecture, idempotency, backpressure, schema evolution. Workflow orchestration experience, both batch and streaming. Comfortable with ambiguity - scoping unclear requests, making speed vs. robustness tradeoffs, communicating transparently when things change. Strong collaboration and communication skills in Hebrew and English