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Strong employer brand and metro location balanced by specialized LLM skillset narrowing applicant pool.
LLM and applied ML skills are broadly transferable across industries with low domain dependency.
Explicit 3+ years applied ML requirement plus mandatory LLM, PyTorch/TensorFlow, and cloud skills.
Design and run experiments and fine-tuning workflows for large language models (LLMs) using Python and ML frameworks.
Support benchmarking, evaluation, and anomaly detection for generative AI models to improve performance.
Contribute to data preparation, documentation of experiments, and application of state-of-the-art evaluation methodologies in collaboration with senior team members.
Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, or related technical field.
0–7 years of overall IT experience including at least 3 years in applied machine learning/data science (internships or academic research count).
Proficiency in Python programming and experience with ML frameworks like PyTorch or TensorFlow.
Basic understanding of LLM architectures, fine-tuning techniques, evaluation metrics, and familiarity with cloud AI/ML services (AWS, Azure, or GCP) and MLOps practices.
Early-career data scientist with foundational hands-on experience in applied ML and Python coding, comfortable working under mentorship.
Strong familiarity with LLM fine-tuning and generative model evaluation methods, able to support experimentation and benchmarking.
Interest in contributing to frontier AI research with a methodical approach to documentation and process adherence.