Source description
About the role
Bachelor's or Master's degree in a quantitative field (computer science, statistics, linguistics, or related) and 2–4 years of applied ML or data science experience, or equivalent practical experience.
Hands-on experience building or improving NLP or LLM-based systems in applied settings.
Familiarity with text classification, information extraction, or other NLP tasks — and an understanding of where these systems fail.
Experience with both prompt engineering and fine-tuning approaches for language tasks, with the judgment to know when to apply each.
Familiarity with modern retrieval strategies and RAG architectures and how they affect LLM system performance.
Experience with Hugging Face Transformers for text classification or related NLP tasks.
Experience contributing to evaluation frameworks, test sets, or performance diagnostics for ML systems, including comfort with statistical methods for measuring model performance.
Proficiency in Python and SQL, and comfort working with structured and unstructured data.
Ability to operate effectively in ambiguous problem spaces — scoping technical approaches when requirements are not fully defined.
Strong written communication skills; able to document systems and findings clearly and present recommendations to non-technical stakeholders.
Experience designing data annotation workflows, labeling guidelines, or label quality processes is a plus.
Experience with model deployment, monitoring, or production ML workflows is a plus.
Familiarity with LangChain and LangSmith or similar LLM orchestration and observability tooling is a plus.
Transportation or logistics industry experience is a plus.
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