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Who we are:
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.
Diversity and inclusion:
We believe in social inclusion, respect, and appreciation of all people. We promote a welcoming work environment, where each CloudWalker can be authentic, regardless of gender, ethnicity, race, religion, sexuality, mobility, disability, or education.
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