Machine Learning Scientist

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 a Machine Learning Engineer on our product support team, you will:

• architect and ship autonomous voice and digital agents that power 10M+ customer interactions per month across West Africa. 
• own end-to-end agent systems from core models through multi-channel orchestration, architecting solutions that work reliably where connectivity is poor, literacy can be low, and most languages lack training data. 
• drive real ownership, from problem discovery to running in production, working alongside product and engineering leaders who prioritize shipping real customer impact.
• shape that future by solving genuinely hard technical problems where systems either work or they fail. 

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.

Our stack (prior experience is a strong plus, but not required):

• backend: Python 3 (+ mypy)
• API layer: GraphQL
• android frontend: Kotlin/Jetpack
• iOS frontend: Swift/SwiftUI
• web frontend: TypeScript/React
• database: Postgres/CockroachDB
• infrastructure: GCP/Terraform
• orchestration: Kubernetes

• This is a fully remote role. Candidates must be based in one of our talent hub countries (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 $152,100 - $178,200 USD, plus a generous equity package.
• Major benefits:
  • Subsidized health insurance for you and your dependents and retirement contributions (both vary from country to country).
  • 6 months of fully paid parental leave and subsidized fertility assistance.
  • Flexible vacation, with most folks taking between 21-30 days exclusive of statutory holidays.
  • $10,000 annual charitable donation matching.

• 5 years of ML experience
• Strong proficiency in Python and common ML libraries 
• Solid theoretical foundation in stats and ML.
• Experience working with voice agents 
• Proven hands-on experience building with LLMs or NLP systems (prompt engineering, RAG, embeddings, fine-tuning, etc.).
• Experience taking ML models from prototype to production and care deeply about reliability, performance, and scalability.
• Fluent in English, (bilingual in French is a big bonus)

• Are comfortable navigating ambiguity and designing ML systems end-to-end without needing extremely detailed requirements.
• Understand the trade-offs between cutting-edge research and pragmatic engineering, and can choose the right tool for the job.
• Like working with large datasets, complex pipelines, and modern ML infra (distributed training, feature stores, monitoring, etc.)

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.

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