Senior Data Scientist with LLM Experience - Remote

Overview

As a Data Scientist on our team, you will contribute to new product development in a collaborative, small-team environment, writing production code for both run-time and build-time applications.

In Short

• Develop and implement LLM-based applications for various use cases.
• Evaluate and maintain data assets and training/evaluation datasets.
• Design and build pipelines for preprocessing, annotating, and managing large-scale text datasets.
• Collaborate with domain experts to understand requirements and ensure ML applications align with business needs.
• Conduct experiments and evaluate model performance to drive continuous improvements.
• Fine-tune and deploy large language models(LLMs) to enhance their performance on specialized tasks.
• Interface with other technical teams to finalize requirements.
• Work closely with development teams to understand complex product requirements and translate them into scalable software solutions.
• Implement development best practices, including coding standards, code reviews, and production-ready implementations.

Requirements

• Practical experience with large language models (LLMs), prompt engineering, fine-tuning RAG-based applications, and benchmarking using frameworks like LangChain.
• Strong background in natural language processing (NLP) with experience using spaCy, word2vec, Flair, BERT.
• Formal training in machine learning, including dimensionality reduction, clustering, embeddings, and sequence classification algorithms.
• Proficiency in Python and experience working with ML frameworks like PyTorch, TensorFlow, and Hugging Face Transformers.
• Experience with cloud platforms such as AWS, GCP, or Azure.
• Understanding of data modeling principles and complex data architectures.
• Experience working with relational and NoSQL databases and vector stores (e.g., MySQL, Postgres, Solr, Elasticsearch, OpenSearch).
• Familiarity with distributed computing frameworks like Spark, Scala, or Ray (highly preferred).
• Knowledge of API development, containerization (Docker, Kubernetes), and ML deployment (highly preferred).
• Hands-on experience with ML Ops/AI Ops, including experiment tracking tools like LangFuse and DVC.
• Experience with deep learning frameworks such as PyTorch, Tensorflow and Hugging Face Transformers.

Benefits

• Opportunity to work in a fast-paced, start-up-like culture while leveraging the resources of an established company.