Focus on understanding existing models, assessing their performance, selecting optimal architectures, and fine-tuning them to meet specific domain and business needs
Collaborate closely with data engineering, product, and domain teams to translate real-world research challenges into scalable, model-driven solutions
Optimize and fine-tune large language models (LLMs) and domain-specific variants using proprietary datasets
Evaluate model performance across key metrics and benchmarks
Implement and operationalize LLM-based and retrieval-augmented systems that enhance Braid-powered products
Assess and select models that best align with domain-specific requirements
Partner with clinical and operational experts to translate research and trial challenges into measurable model evaluation frameworks

Requirements

MS in Machine Learning, Computer Science, or related quantitative discipline
5+ years of hands-on experience developing and fine-tuning ML or LLM models
Demonstrated expertise in Python, with experience and knowledge of a commercial framework like PyTorch
Hands-on experience developing, managing, and troubleshooting workflows within Databricks for data engineering, analytics, and machine learning projects
Documented strong understanding of the ML lifecycle
Experience with embeddings and retrieval-augmented generation (RAG)

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learninglarge language modelsmodel fine-tuningPythonPyTorchembeddingsretrieval-augmented generationML lifecycledata engineeringanalytics

Soft Skills

collaborationproblem-solvingcommunicationresearch translationperformance assessment

Certifications

MS in Machine LearningMS in Computer Science