Machine Learning Engineer

Job description

This isn’t your regular job. Almedia is a place where those who want to push harder can accelerate their careers faster than anywhere else. We’re aiming to become Germany’s second bootstrapped unicorn. Almedia is already Europe’s #3 fastest-growing company in 2025 (FT1000). We are building the future of marketing by rewarding our community of over 70 million users for engaging with our advertisers’ products. We are offering a new way to acquire users for the biggest companies in the world. MACHINE LEARNING ENGINEER £130K – £230K • Offers Equity WHY ALMEDIA Our team has a track record of shipping ML that moves the business, not research for its own sake. Almedia runs Freecash, a rewards platform with ~80 million users, profitable since day one and currently the #3 fastest-growing company in Europe. We operate lean: a small data/ML org, a direct line from what you build to what the company earns, and plenty of room to shape our infrastructure as we grow it together. If you like the idea of a fast feedback loop, build a model, test it, ship it, see the number move, all within weeks rather than quarters, this is that kind of team. We're hiring across a range of seniority for this role, and welcome applications whether you're an experienced Senior engineer or ready for Staff or Principal-level scope. You can be based in either London or Berlin, and we support relocation and visa sponsorship for the right candidate. THE ROLE You'll take ownership of designing, building, and shipping production-grade ML systems that power how Almedia ranks offers and personalises rewards for users in real time. This is a hands-on role; you'll be in the code, building alongside a small, high-ownership team. We're growing our ML infrastructure together, so you'll have real influence over how it's built, from the training pipeline through to the model that runs on it. We're looking for someone with real depth in production ML who's energised by variety, someone who treats an unfamiliar part of a problem as a chance to dig in, rather than needing to stay within one familiar slice of it. Priorities here shift as the business needs them to, moving across our reward-ranking, personalisation, and measurement work, and we want someone who finds that exciting, not distracting from their specialty. WHAT YOU'LL WORK ON - Designing and shipping the models that decide which offers and rewards get shown to which users, in real time - Translating the mathematical problems at the core of our business into concrete ML formulations - Building the systems behind reward/payout structures, balancing user motivation, cost sustainability, and advertiser value - Diagnosing underperforming reward campaigns and identifying the causes - Applying statistical and causal methods (A/B testing, uplift, causal inference) to make sure what you ship actually works, not just looks good in a demo - Partnering directly with the people setting business priorities; you'll help shape what gets built, not just execute a spec WHAT WE'RE LOOKING FOR - Genuinely hands-on production ML experience, particularly on non-standard problems; you enjoy building and shipping models end to end - Comfortable working across the full stack of a problem: from data pipeline to model to live serving - Strong grounding in statistics (A/B testing, regression, probability) and, ideally, causal inference - Genuine enthusiasm for building infrastructure alongside your team, not just using infrastructure that already exists - A genuine problem-solver's mindset; you're energised by tackling whatever the business needs next, and see moving across problems as a chance to learn rather than a distraction from your specialty - Motivated as much by impact as by technical elegance; happy to do the simple, high-leverage thing when that's what the business actually needs BONUS POINTS FOR - Experience in adtech, gaming, gambling, or other high-scale personalisation/monetisation systems - Familiarity with recommendation systems or ranking models specifically - Familiarity with gaming/growth KPIs like pLTV, retention, and ROAS - Any exposure to applying LLMs or agentic tooling to production ML workflows (not as your primary focus, but as evidence of a builder's curiosity) We believe in fostering talent, evaluating all skill levels during the hiring process, and providing a clear path for growth. Almedia is an equal opportunity employer. We embrace and celebrate diversity, and encourage individuals from all backgrounds to apply.