We believe trading has become so complex that machines will be fundamentally better that humans. As such, Alphalupe is developing a fully systematised machine learning approach into trading any financial security. After a period of beta testing we have now received investment and are operating a fund comprised of some of the most sophisticated wealth managers in the world. We have a vision and a clear path to achieve 1 billion AUM in the next 24 months.The ChallengeWe are going to deeply understand how the economy works, how investors deploy their capital, and how capital markets works. Furthermore, the market is full of trading algorithms, knowledge human traders, but also biased, occasional traders. Together they move the market and predicting their behaviour is a non-trivial task. Success is not guaranteed. But with intelligence, agility, action and perseverance we will make big strides.As an early stage startup we will be build the culture, processes and product so you should be ready to be flexible.How we WorkWe move fast, but don’t break things as we are responsible for our customers assetsWe attribute high ownership but expect high communicationWe are frugal and believe that constraints spark innovationWe like sharing and helping others, but we measure ourselves by what we do and achieve.We prefer being in the office and all its serendipitous events that lead to innovationWe are honest, trustworthy, adult problem solvers but we have low egos, don’t like drama nor toxic office politicsWe are focused on our product, solve the right problems and navigate away from pitfallsWhat you will BringWe are looking for deep expertise in several machine learning techniques and practical experience in managing the lifecycle of your models. You will be responsible for understanding the requirements, creating / testing / training your solutions and to deploy them into production. We look for people that can quickly learn new fields and that have shown that before.RequirementsProven experience in developing and deploying of ML models in the pastExtensive experience using ML frameworks like PyTorch / TensorflowProduct mindset and deep understanding of data and model lifecyclesExperience building at scale in Python / numpy / pandas or othersExperience with ETL, data management, data augmentation and data engineeringOrchestration tools and infrastructure knowledge (AWS or others)Bonus requirementsFinancial industry experienceExperience with transformers and embeddingsA degree (PhD) from a global top university