Join the CloudWalk Wolfpack as a LLM Data Scientist.
Your Mission:As an LLM Data Scientist at CloudWalk, your role is central to exploring and expanding the capabilities of Large Language Models (LLMs), including the specialized area of Retrieval-Augmented Generation (RAG) and overall retrieval-generated technologies. This encompasses staying abreast with the latest research in the field, writing full pipelines for distributed training across various tasks and models simultaneously, and delving deep into human reasoning and cognition through the lens of advanced AI technologies.
In this position, you will bridge the gap between theoretical AI research and practical, scalable solutions in the fintech sector, with a specific focus on the integration of retrieval mechanisms in LLMs. Scaling models is a critical part of your role.
You will be responsible for training models on a large scale, managing the complexities of deploying these sophisticated systems across extensive hardware infrastructures. This will involve not only a deep understanding of the technical aspects of machine learning, NLP, and retrieval systems but also a strategic approach to resource management and performance optimization.
Your work involves not only technical skill but also intellectual curiosity. You will delve into profound questions that are at the forefront of AI research and development: Is human knowledge embedded in language? Can transformers and retrieval-augmented models learn non-linguistic patterns? What is the relationship between language, consciousness, and AI-driven insights? How does the integration of retrieval systems enhance the capabilities of LLMs in understanding and generating human-like responses?
Your contributions will be pivotal in advancing CloudWalk's mission to revolutionize financial technologies through cutting-edge AI, particularly through the innovative use of retrieval-augmented language models.

What You'll Do:

• Fine-tune and deploy Large Language Models (LLMs), including Retrieval-Augmented Generation (RAG), to develop cutting-edge workflow tools that directly impact millions of customers every day.
• Be an integral part of a dynamic, startup-like team within our R&D department, thriving in an environment of chaos, camaraderie, and creativity.
• Continuously absorb and apply the latest in NLP, LLMs, and RAG, driving innovation and excellence in our solutions.
• Experiment, research, and benchmark to refine model architectures, optimizing performance and efficiency with a specific focus on the integration of retrieval systems in LLMs.
• Technologies you will work with on a daily basis include: Hugging Face's libraries (such as Transformers, Accelerate, Datasets, and PEFT) and CLI, PyTorch's torchrun, GCP for Cloud Computing, Kubernetes for scalability, Tim Dettmers' bitsandbytes, standard data analysis tools (Pandas, NumPy, SciKit-Learn, Matplotlib), Git, vector databases (Qdrant, Pinecone), and Bash scripting.
• Bonus: we love Weights and Biases and MLflow.

Key Responsibilities:

• Innovate and improve LLM training and serving methodologies, with a special emphasis on integrating retrieval mechanisms in the models.
• Stay relentlessly updated with industry trends, new techniques, and novel applications of LLMs, particularly in the context of RAG.
• Develop, test, and deploy LLMs, including those with retrieval-augmented capabilities, for various generation tasks, ensuring seamless integration and high performance.
• Share your knowledge and findings on RAG and other LLM advancements, fostering a culture of learning and growth within the team.
• Tackle challenges head-on during model training and deployment, including the unique aspects of integrating retrieval systems with generative models, ensuring smooth and effective resolutions.

What We Expect From You:

• While we are looking for someone with a strong foundation in LLMs and RAG, we understand that the field of AI is vast and constantly evolving. We value candidates who have a solid base in some of the key areas and are enthusiastic about learning and growing in others.
• Foundational Knowledge: A strong understanding of machine learning, NLP, and basic principles of neural networks. Familiarity with the concepts behind large language models and retrieval-augmented generation is a plus.
• Technical Experience: Hands-on experience with some of the technologies we use, such as Hugging Face’s libraries, PyTorch, GCP, Kubernetes, and data analysis tools. If you’ve worked with vector databases or have experience in Bash scripting, that’s a bonus.
• Problem-Solving Skills: The ability to tackle complex challenges, think strategically about resource management, and optimize model performance.
• Learning Attitude: A keen interest in staying updated with the latest trends and developments in AI and the willingness to continuously learn and apply new techniques and methodologies.
• Team Collaboration: Strong communication skills and the ability to work collaboratively within a diverse team, sharing knowledge and contributing to a learning culture.

We understand that not every candidate will be an expert in all these areas. What's more important is your eagerness to learn, adapt, and contribute to our mission. We value diversity in skills and backgrounds and believe in nurturing talent to reach its full potential.
Why CloudWalk?Join us, and you're not just joining a company; you're joining a movement. A movement that's redefining fintech with innovation and a team that's as diverse in its talents as it is united in its vision. With CloudWalk, you don't just build a career; you contribute to a mission that's transforming the financial landscapes for thousands.Dare to dream, dare to create, dare to join the Wolfpack. Apply now!