Applied AI Scientist - LLMs & Voice

Requirements

• 5+ years of experience in AI/ML experience
• Proven hands-on experience building with LLMs or AI systems (prompt engineering, RAG, embeddings, fine-tuning, safety and guardrails etc.).
• Strong foundations in statistics and ML theory.
• Solid Python and backend engineering skills.
• Track record taking AI/ML systems from prototype to production and care deeply about reliability, performance, and scalability.
• Fluency with AI coding agents, with bonus points for deploying them as part of production systems
• Bonus points:
• Research background in speech or LLMs
• Proven Voice AI experience (ASR, TTS, latency optimization, real-time streaming, voice agents)
• Experience building customer support or conversational AI systems
• You follow the latest research in speech and LLMs, and know how to translate papers into production wins
• Familiarity with low-resource language challenges
• Building in high-risk agent environments like finance
• Fluent in English, (bilingual in French is a big bonus!)

You might be a good fit if you

•

• Are comfortable navigating ambiguity and designing AI systems end-to-end without detailed specs
• Obsess over evaluation – you've built frameworks to catch hallucinations, measure groundedness, and know when to trust your system
• Balance research intuition with pragmatic engineering; you ship working systems, not just prototypes.
• Like working with large datasets, complex pipelines, and modern ML infra (distributed training, feature stores, monitoring, etc.)

About engineering at Wave

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.

Recommended (optional)

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