CloudWalk is a fintech company reimagining the future of financial services. We are building intelligent infrastructure powered by AI, blockchain, and thoughtful design. Our products serve millions of entrepreneurs across Brazil and the US every day, helping them grow with tools that are fast, fair, and built for how business actually works. Learn more at cloudwalk.io.
Who We’re Looking For:
We’re hiring a Data Scientist focused on Large Language Models to join our AI R&D team. You’ll work on designing, training, and optimizing LLMs that power real product features and internal tools. You’ll touch everything from architecture and data preparation to multi-scale training and evaluation.
This is not a prompt engineering or RAG-focused role. We’re looking for someone who gets the math, understands the models, and knows how to write code that scales.
You’ll work side-by-side with engineers, MLOps, and product teams to make our LLMs smarter, faster, and more useful.
What You'll Do:
- Train and fine-tune LLMs using multi-GPU and distributed setups.
- Analyze model performance, debug failures, and implement improvements.
- Work with curated and synthetic datasets, including classification and generation tasks.
- Design experiments, track results, and iterate quickly with tools like MLflow.
- Write clean, production-ready code and collaborate via GitHub.
- Push boundaries: think about architecture, memory efficiency, scale, and cost.
What We’re Looking For:
- LLM understanding: You know how these models work under the hood - transformer internals, tokenization, embeddings, etc.
- Stats and ML fundamentals: You have a solid foundation in statistics, machine learning, and optimization.
- Coding skills: You write Python well. You’ve worked with PyTorch or JAX. You don’t fear Bash or git.
- Training experience: You’ve trained models beyond notebooks. Bonus if you’ve worked with mixed precision, DeepSpeed, or multi-node training.
- Systems mindset: You understand trade-offs - throughput vs memory, latency vs accuracy.
- Good judgment: You know when to read a paper, when to read a stack trace, and when to rewrite the dataloader or part of the LLM architecture.
Bonus Points:
- Experience with Hugging Face Transformers, Datasets, Accelerate.
- Familiarity with Kubernetes, Ray, or custom training infra.
- Exposure to embeddings, classification tasks, or token-level losses.
- Ability to mentor or guide junior researchers or engineers.
How We Hire:
- Online assessment: Online assessment: technical logic and fundamentals (Math/Calculus, Statistics, Probability, Machine Learning/Deep Learning, Code).
- Technical interview: dive into theory and reasoning (no code).
- Cultural interview.
If you are not willing to take an online quiz, do not apply.
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