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Lead Machine Learning Engineer

We Are Redefining How People Approach Their Health


ZOE is the science and nutrition company leading a movement to transform the health of millions.


We exist because the food we eat is making us sick. Most of what we are taught about food is wrong.


ZOE runs the world’s largest nutrition science study to find scientifically proven solutions.

Our randomised controlled trial of ZOE proves that if you eat the right food for your body, you can feel healthier in weeks and be on track for more healthy years.


ZOE can change the way you eat, feel, and live. We host world-leading scientists on our podcast and bring proven science to your plate with Daily30+, our 30+ plant supplement.


Over 100,000 people rely on ZOE Membership, our personalised nutrition program, to make smarter food choices. ZOE Membership turns complex science into clear step-by-step actions, helping you improve your health with every meal.


ZOE means life — and you can change your life with food.


Visit our career page and become a ZOEntist 🚀


About the Team

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! If you're passionate about cutting-edge machine learning, building impactful products, and solving real-world problems, this is the role for you.


About the Role

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.


What You’ll Do…
  • 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!


What makes you a great fit...
  • 7+ years of experience in backend software engineering
  • 5 years of experience working on a deployed ML or AI product.
  • A strong background in ML, Computer Science, Mathematics, or Statistics—either through a degree or equivalent experience.
  • Experience working on a large scale backend system, dealing with scaling challenges, high availability, or supporting a large numbers of users
  • Passionate about LLMs,prompting techniques, and evaluation strategies.
  • Experience in programming languages such as Python or Java.
  • Understanding of cloud platforms (Google Cloud, AWS, Vertex AI, Sagemaker).


Bonus points!
  • Experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn.
  • Experience with data analysis or data science


The experience, skills, and attributes listed above reflect what we believe will contribute to success in this role. If you're passionate about ZOE and the opportunity, but don't meet 100% of the criteria, we still encourage you to apply. We are committed to supporting growth and are happy to offer up-skilling opportunities where possible.


Remote Philosophy

ZOE is a remote-first company, meaning remote work isn’t just an option — it’s how we work best. We are intentional about building a distributed, high-performing team where collaboration, trust, and flexibility thrive.

We design our workflows around asynchronous communication and shared documentation to support autonomy, focus, and cross-timezone collaboration. While our teams work independently, connection and teamwork remain central to how we operate — through regular rituals, meaningful virtual interactions, and in-person gatherings every quarter. These include team offsites and a yearly company-wide retreat to build relationships, spark creativity, and have fun together.

Being remote-first also means we value outcomes over hours and trust our team members to manage their work in a way that suits their unique rhythm and responsibilities. This approach allows us to support a truly flexible work environment, while staying aligned with our mission and values.

At ZOE, working remotely doesn’t mean working alone — it means being empowered, supported, and connected, wherever you are.


Compensation Philosophy

We are committed to offering competitive and equitable compensation that reflects the value of each role and aligns with regional labor market standards. Our approach to compensation goes beyond just base salary — we offer a comprehensive package that includes base pay and stock options, ensuring that every team member is rewarded for their contributions to the company’s growth and success.

We believe that building a thriving team requires not only providing fair and competitive compensation but also fostering an environment where success is shared collectively. Our total compensation package is designed to support the well-being of our employees, recognise their individual contributions, and empower them to grow alongside ZOE.


Benefits & Perks

We understand the significant role our benefits play in motivating, inspiring and safeguarding our employees' well-being. Our benefits strategy is thoughtfully designed to echo our mission and values, recognising the diverse needs arising from different life stages of our ZOEntists.

Our approach to benefits takes an inclusive and flexible view of both personal and professional growth. From competitive health insurance and wellness packages to inclusive parental policies, building connection, and tailored professional development programs, we've got you covered.

At ZOE, we continue to build a benefits package that invests in our team members’ long-term personal and professional growth and wellbeing, adding to this list as it evolves.


Equal opportunities

We are committed to fostering a diverse and inclusive team where every individual can bring their authentic self to work. We believe that this is key to our success and are dedicated to positively impacting the tech industry. As part of our commitment to equal opportunities, we encourage candidates from underrepresented backgrounds to apply. We ensure a respectful and inclusive environment for all and do not discriminate on the basis of race, colour, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, marital status, disability, or age. If you require any accommodations during the interview process, please feel free to inform us, and we will make every effort to support your needs.

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What You Should Know About Lead Machine Learning Engineer, Zoe

At ZOE, a pioneering science and nutrition company, we're transforming how individuals approach their health, and we're on the lookout for a Lead Machine Learning Engineer! This remote role, open to candidates across the UK and EU, places you at the heart of our innovative Snap team, where AI-driven solutions make food logging a breeze—just snap a picture! In this position, you'll collaborate with a passionate team of ML engineers, data scientists, and product managers to tackle complex problems as you build and scale our photo logging AI product. Your expertise will drive impactful changes for over 100,000 ZOE members who rely on personalized nutrition for healthier living. Bring your 7+ years of backend software engineering experience, particularly in deployed ML or AI products, and be prepared to dive into the latest in machine learning research, model performance, and system scaling. If you're excited about advancing user health through personalised nutrition and enjoy solving real-world challenges, ZOE invites you to join us and make a difference!

Frequently Asked Questions (FAQs) for Lead Machine Learning Engineer Role at Zoe
What are the responsibilities of the Lead Machine Learning Engineer at ZOE?

As a Lead Machine Learning Engineer at ZOE, you will be responsible for building and scaling our photo logging AI product, working closely with data scientists, software engineers, and domain experts to define project goals. You will evaluate model performance, conduct A/B testing, and iterate to enhance model accuracy. Addressing complex back-end issues while collaborating with product managers and UX designers is essential, ensuring we offer a best-in-class user experience.

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What qualifications are required for the Lead Machine Learning Engineer position at ZOE?

To excel as a Lead Machine Learning Engineer at ZOE, you should possess over 7 years of backend software engineering experience alongside at least 5 years in deployed ML or AI products. A strong foundation in ML, Computer Science, Mathematics, or Statistics is crucial, whether through a degree or practical experience. Familiarity with cloud platforms and programming languages like Python or Java, as well as experience with ML frameworks, is also highly desirable.

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How does ZOE support professional growth for a Lead Machine Learning Engineer?

At ZOE, we are committed to professional development. As a Lead Machine Learning Engineer, you'll have access to up-skilling opportunities tailored to your growth. We believe in fostering a collaborative environment that encourages learning from peers and industry advancements, ensuring you remain at the forefront of ML technology and methodologies.

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What is ZOE's working culture for remote employees?

ZOE embraces a remote-first culture where collaboration, trust, and flexibility thrive. We prioritize asynchronous communication and shared documentation to support autonomy across time zones. Our team enjoys regular interactions, including virtual rituals and quarterly in-person gatherings, fostering a sense of connection while allowing you to manage your work rhythm effectively.

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What is the compensation philosophy for the Lead Machine Learning Engineer role at ZOE?

ZOE's compensation philosophy ensures that team members receive competitive and equitable pay that aligns with regional market standards. In addition to base salary, we offer a comprehensive benefits package, including stock options, to reward contributions to the company’s success and promote employee well-being.

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Common Interview Questions for Lead Machine Learning Engineer
Can you describe your approach to building and deploying machine learning models?

When discussing my approach to building and deploying machine learning models, I emphasize the importance of understanding the problem domain first. I start by clearly defining project goals and collecting data effectively. Throughout the process, I ensure to monitor model performance continuously, and I prioritize iterative improvements based on user feedback and A/B testing results.

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What experience do you have with large-scale machine learning systems?

In previous positions, I have worked extensively on large-scale ML systems, addressing challenges such as high availability and dealing with multiple users. I focus on optimizing backend architectures and ensuring that the models I deploy can handle increased load without sacrificing performance or accuracy.

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How do you stay updated on the latest machine learning research and trends?

To stay current with the ever-evolving field of machine learning, I regularly read research papers, attend industry conferences, and participate in webinars. Additionally, I enjoy engaging with online communities and forums where professionals share insights on recent developments and practical applications, which helps me integrate cutting-edge techniques into my work.

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Describe a challenging machine learning problem you solved and the outcome.

One challenging problem I encountered involved improving the accuracy of an image classification model that initially had a low success rate. I performed extensive data augmentation and implemented advanced tuning techniques, resulting in a significant boost in accuracy. The final model not only surpassed the expected performance metrics but also positively influenced user engagement with the product. This experience taught me the value of persistence and innovation in solving complex challenges.

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What programming languages are you most comfortable with for ML development?

I have a strong command of Python, which I primarily use for machine learning development due to its rich ecosystem of libraries such as TensorFlow and scikit-learn. I’m also proficient in Java and appreciate its scalability, particularly when working on backend systems. This proficiency allows me to choose the right tools for the task at hand.

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How do you handle feedback on your machine learning models from stakeholders?

I view feedback from stakeholders as an essential part of the machine learning development cycle. I make sure to maintain open channels of communication, actively listening to their insights and concerns. I then use this feedback to iterate on model improvements, ensuring that the final product aligns with user needs and expectations.

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What experience do you have working in a remote team?

I have enjoyed working within remote teams for several years, and I find that clear documentation and regular check-ins are key to ensuring everyone is aligned on project goals. I believe in fostering a collaborative spirit through virtual platforms and encourage brainstorming sessions, which can lead to innovative solutions.

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What are your thoughts on model evaluation strategies?

Model evaluation is crucial to ensure that the predictions are reliable. I typically utilize a variety of metrics, such as accuracy, precision, recall, and F1 score, depending on the problem at hand. I also advocate for A/B testing in production to assess real-world performance before fully deploying updates.

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How do you approach continuous improvement for machine learning models post-deployment?

Continuous improvement is vital for machine learning models. After deployment, I closely monitor performance metrics and gather user feedback. If a model's performance declines, I initiate a review process that includes analyzing incoming data and retraining the model as needed, ensuring it remains effective and relevant.

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What motivates you to work in the field of machine learning?

I'm driven by the potential of machine learning to solve real-world problems and improve the lives of people. The ever-evolving nature of the field keeps me engaged and inspired, as I thrive on the challenges of creating innovative solutions that can significantly impact user experiences.

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Full-time, remote
DATE POSTED
April 22, 2025

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