Lead Machine Learning Engineer, ZOE

• If you're passionate about cutting-edge machine learning, building impactful products, and solving real-world problems, this is the role for you
• 7+ years of professional experience in backend software engineering, with at least 5 years explicitly focused on the full lifecycle of deployed Machine Learning or AI products
• Proven expertise in designing, building, and extensively refactoring production-grade ML codebases, demonstrating strong command of software engineering best practices for complex, stateful systems
• Deep practical expertise working with productionised LLM based products
• Experience with vector databases and similarity search
• Hands-on experience with embedding models and their fine-tuning for semantic search
• Prompt engineering
• Demonstrated ability to diagnose and troubleshoot complex issues within ML pipelines, including performance, memory management, and data-related challenges
• Strong command of Pythonor Java
• Extensive experience working on large-scale backend systems, dealing with scaling challenges, high availability, and supporting a high volume of users in an ML context
• Hands-on experience with MLOps practices, including model deployment, monitoring (e.g., drift, quality), versioning, and A/B testing ML systems in production
• Proficiency with cloud platforms such as Google Cloud (preferred, including Vertex AI) or AWS (including SageMaker)

Desirable

• At ZOE, we're on a mission to empower people with the most advanced science and technology to transform their health
• Our Snap team is at the heart of this mission, developing AI-driven solutions that make food logging effortless—just by using a camera!
• We're looking for an Lead Machine Learning Engineer to join our team of ML engineers and data scientists, helping to scale our product to millions of users, and working on some complex technical problems
• You'll be tackling a range of challenges that will require analytical thinking and keen instincts, as well as an understanding of our domain and how our users behave
• You'll work with a cross-functional team including product managers, designers, and front-end engineers to deliver our cutting-edge photo logging product
• Build and scale our photo logging AI product, a core feature for ZOE’s members
• Work alongside data scientists, software engineers, and domain experts to collect requirements and establish project goals
• Evaluate model performance, conduct A/B testing, and iteratively improve model accuracy and efficiency
• Work with our platform team and collaborate on hosting, availability and scale challenges
• Stay current with the most recent developments in machine learning research, methodologies, and technologies, integrating them seamlessly into our workflow
• Solve complex backend problems while collaborating closely with product managers and UX designers to provide a best-in-class customer experience
• Write code across our micro-services and data pipelines
• Ship in production, preferably daily or more!

• 👋 Stage 1: Talent Acquisition Screen – 40 mins A friendly chat with a member of our Talent team to understand your experience, career aspirations, and motivations for joining ZOE. This is also a great opportunity for you to ask any initial questions about the role or our culture
• 🧠 Stage 2: Hiring Manager Screen – 30 mins A focused discussion with the hiring manager to delve deeper into your past leadership experiences, your approach to building and scaling ML products, and your initial thoughts on the challenges and opportunities at ZOE
• 🎯 Stage 3: Remote Interview Loop This final stage includes three focused interviews to assess your technical and leadership capabilities. These are typically spread over three days, but we’re flexible — we can combine them into one or two days depending on availability. You’ll need to be successful in each interview to progress to the next. We’ll keep you informed with feedback and next steps after each session. 📊 ML Engineering Coding Interview | 60 mins Hands-on coding using a Retrieval-Augmented Generation (RAG) codebase. We’ll assess your ability to debug, analyze, and refactor. You’re welcome — and encouraged — to use AI assistants (like GitHub Copilot or ChatGPT), as that reflects how we work day to day. ️ System Design Interview | 60 mins You'll design a scalable ML system from scratch. We’re looking to understand your architecture thinking, MLOps knowledge, and how you make and justify trade-offs. 💬 Behavioural & Leadership Interview | 45 mins A conversation to explore how you lead teams, navigate complexity, and align with ZOE’s values. We’ll discuss non-technical problem-solving, collaboration, and leadership principles.
• 🎉 Offer Stage We like to move fast, keep things clear, and make sure you feel supported every step of the way! If you're the right fit, we'll extend an offer and guide you through the next exciting steps