At a Glance
- Tasks: Design and build cutting-edge ML systems for marketing workflows.
- Company: Join Passionfruit, a pioneering AI-native operations company.
- Benefits: Enjoy competitive pay, flexible hybrid work, and growth opportunities.
- Other info: Work in a dynamic London office with a collaborative team.
- Why this job: Make a real impact on marketing teams with innovative AI solutions.
- Qualifications: Experience in ML systems, Python, and a passion for tech.
The predicted salary is between 60000 - 80000 £ per year.
About Passionfruit
We’re building AI-native operations for marketing teams - automating the work that slows them down, from campaign briefs and performance reporting to creative review, compliance workflows, and paid media analysis. Our platform is purpose-built for marketing, trained on real marketing workflows and deeply integrated with ad platforms, CRMs, brand assets, and analytics tools.
As an ML Engineer, you’ll design, build, and ship production ML systems that power our core product and agent ecosystem - from real-time chat and workflow automation to autonomous agents like daily performance briefings and competitor tracking.
What you’ll work on:
- Develop and deploy ML/LLM systems for marketing-specific workflows (reporting, analysis, review, compliance)
- Build agentic systems with persistent memory, tool use, and workflow orchestration
- Integrate models with live data sources (Google Ads, GA4, Meta Ads, CRM, analytics)
- Improve retrieval, evaluation, and fine‑tuning on marketing‑domain data
- Collaborate closely with product and engineering to ship reliable, user‑facing AI
What we’re looking for:
- Strong experience building and shipping ML systems in production
- Hands‑on experience with LLMs, RAG, agents, or workflow‑driven AI systems
- Solid engineering fundamentals (Python, APIs, data pipelines, evaluation)
- Comfort working in a fast‑moving product environment
- Bonus: experience with marketing, ads platforms, or analytics data. Also ideal if you’ve worked with Elixir.
You’ll have meaningful ownership, real user impact, and the chance to define how AI actually works inside modern marketing teams.
Working hours and location:
Location: London, Chancery Lane
Hours: 9-6pm
For more information or to apply to this role please contact.
Machine Learning Engineer Developer in London employer: usepassionfruit.com
At Passionfruit, we pride ourselves on being an exceptional employer that fosters innovation and collaboration in the heart of London. Our hybrid work culture promotes flexibility while ensuring meaningful ownership and real user impact for our Machine Learning Engineers. With a focus on employee growth and development, we offer unique opportunities to shape the future of AI in marketing, making every day at Passionfruit both rewarding and impactful.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer Developer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those related to marketing workflows. This will give potential employers a taste of what you can do and how you can contribute to their team.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice common ML scenarios and be ready to discuss how you've tackled challenges in past projects.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are genuinely interested in joining our mission.
We think you need these skills to ace Machine Learning Engineer Developer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Machine Learning Engineer role. Highlight your experience with ML systems, LLMs, and any relevant projects you've worked on. We want to see how you can contribute to our mission!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about AI in marketing and how your background makes you a perfect fit for our team. Keep it engaging and personal – we love to see your personality come through!
Showcase Relevant Projects:If you've worked on any projects related to ML or marketing workflows, make sure to include them in your application. We’re keen to see real examples of your work and how you’ve tackled challenges in the past. It helps us understand your hands-on experience!
Apply Through Our Website:We encourage you to apply directly through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to submit all your materials in one go. Plus, it shows us you’re serious about joining our team!
How to prepare for a job interview at usepassionfruit.com
✨Know Your ML Stuff
Make sure you brush up on your machine learning fundamentals. Be ready to discuss your experience with building and shipping ML systems, especially in production. Prepare examples of projects where you've worked with LLMs or workflow-driven AI systems.
✨Showcase Your Engineering Skills
Since solid engineering fundamentals are key, be prepared to talk about your proficiency in Python, APIs, and data pipelines. You might even want to bring along a portfolio or code samples that demonstrate your technical abilities.
✨Understand the Marketing Landscape
Familiarise yourself with marketing workflows and tools like Google Ads and GA4. Being able to speak knowledgeably about how ML can enhance these processes will show that you’re not just a techie but also understand the business side of things.
✨Be Ready for Collaboration
This role involves working closely with product and engineering teams, so be prepared to discuss how you’ve collaborated in the past. Share examples of how you’ve contributed to team projects and how you handle feedback and iteration.
