Research Engineer

📍 Paris | Full-time | Fluent 🇫🇷 & 🇬🇧   At Bigblue, we're building the logistics backbone for the next generation of commerce. Modern brands sell everywhere: through their own online stores, marketplaces, retail, social commerce, and more. Across every channel, customers expect the same fast, reliable, and transparent experience after they buy. What used to be Amazon's exclusive advantage is now becoming the standard for every ambitious brand. We're helping them get there. Since 2018, we've built a tech-driven fulfilment platform used by 600+ brands, including Muji, Aigle, Scuffers, and Cabaïa. With 200+ Bigbluers across the UK, France, Spain, and Germany, our proprietary tech stack, and a network of 9 warehouses and over 100,000 sqm of fulfilment space across Europe, we ship millions of orders every month. And we're nowhere near done! Backed by €20M+ in funding, we're expanding across Europe and building the operating system that will power modern commerce operations at a global scale. At Bigblue, we hold ourselves to a very high bar: in the quality of our product, the rigour of our operations, and the care we bring to every merchant we work with. You'll be working alongside talented people on real, high-impact problems, in an environment where high standards come with genuine support, ownership, and room to grow. If you want your work to matter from day one, you're in the right place.     The Role   As an Applied Scientist, you will develop and maintain efficient and robust algorithms that significantly improve our warehouse operations. You'll work at the intersection of research and engineering, identifying optimization opportunities, collaborating with stakeholders on operational feasibility, and shipping solutions that have immediate, visible impact on our European fulfillment network.   This is probably one of the highest-leverage job openings we have today – each percentage of performance we squeeze out of our algorithms allow us to scale even faster.   What You Will Work On Identify optimization opportunities across our WMS algorithms Align with stakeholders on operational feasibility and discuss tradeoffs Develop and maintain efficient and robust algorithms that significantly improve our operations Collect feedback, measure performance and iterate based on real-world results