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Wave is seeking an Applied AI Scientist to advance our Support Automation product, empowering millions of users across West Africa. You will bridge research and production, build robust agent systems, and tackle edge cases like poor connectivity and low literacy while collaborating with product and engineering to ship real customer impact.
This is a fully remote role with annual travel to operating markets.
We’re making Africa the first cashless continent.
In 2017, over half the population in Sub-Saharan Africa had no bank account. That’s for good reason—the fees are too high, the closest branch can be miles away, and nobody takes cards. Without access to financial institutions, people are forced to keep their savings under the mattress. Small business owners rely on lenders who charge extortionate rates. Parents spend hours waiting in line to pay school fees in cash.
We’re solving this by building financial services that just work: no account fees, instantly available, and accepted everywhere. In places where electricity, water and roads don’t always work, you can still send money with Wave. In 2017, we launched a mobile app in Senegal for cash deposit, withdrawal, and peer-to-peer and business payments. Now, we have millions of users across 9 countries and are growing fast.
Our goal is to make Africa the first cashless continent and that’s where you come in…
Wave is now the largest financial institution in Senegal and Côte d’Ivoire, with millions of users, growing rapidly year on year. And, we’re still in the early days of our product roadmap and potential impact on people’s everyday lives.
We’re helping millions of customers across West Africa access financial services through mobile money, and great support is fundamental to that. We believe the future of customer support lies in machines handling boring repetitive tasks so humans can focus on high value interactions that require empathy and creativity
As an Applied AI Scientist on our Support Automation product team, you will:
If you’re energized by ownership, thrive on deep technical challenges, and want to build infrastructure that serves millions in emerging markets, let’s talk.
This is a fully remote role. Candidates must be based in one of our talent hub countries (US, UK, Spain, Kenya and Ghana) or in one of our operating markets in Africa including Senegal, Côte d’Ivoire, or Burkina Faso.
Wave provides a yearly $1,200 stipend to support coworking meetups with teammates.
Remote team members are expected to travel to our operational markets (e.g. Senegal or Côte d’Ivoire) at least once a year. Exceptions apply, but we’ve found this key to understanding our users and product.
Our salaries are competitive and are calculated using a transparent formula. For this role, depending on your level and location, we offer a salary between $167,00 - $227,900 USD, plus a generous equity package.
Major benefits:
5+ years of experience in AI/ML experience
Proven hands‑on experience building with LLMs or AI systems (evaluation, RAG, embeddings, safety and guardrails etc.) .
Strong foundations in statistics and ML theory.
Solid Python skills.
Track record taking AI/ML systems from prototype to production and care deeply about reliability, performance, and scalability.
Fluency with AI agents, with bonus points for deploying them as part of production systems
Bonus points:
We care about the big picture. We don’t hire engineers to just ship tickets. We hire them to solve problems. That means caring deeply about outcomes, understanding context, and jumping in wherever something’s broken, even if it’s technically “not your area.” When we see problems, inefficiencies, or opportunities to make something better, we act. We dig into operational issues, clarify fuzzy product specs, or step into unfamiliar code to help unblock teammates.
We move as fast as possible. Speed matters. It lets us try things quickly, get feedback early, and course-correct while it’s cheap. So we write small PRs. We aim for MVPs. We leave TODOs and file follow-ups. We don’t over-perfect v1. That said, we’re building a financial product. Some things—like money movement, correctness, or security—deserve more caution.
We like boring technology. We favor tools that are reliable, well-understood, and easy to debug. This keeps us focused on solving meaningful problems instead of wrestling with unpredictable infrastructure. If a new technology helps us move faster, build safer, or solve a real need, we’ll consider it. But we don’t adopt tools just because they’re new—we adopt them because they’re right.
Simplicity is a strategy. It lets us focus our energy where it matters most: serving our users.