人工智能工程师

Design and deliver applied AI systems (LLMs/CV/multimodal) that automate clinical variable extraction and clinical note generation. Work includes repeatable validation, robust documentation, and HITL feedback loops. Strong emphasis on data engineering and backend rigor to ensure model usability and efficiency.

MSc/PhD in CS, EE, Applied Math, Stats, Physics, or equivalent depth via experience. 2–5+ years in AI/ML engineering or applied data science. Healthcare or clinical workflows experience strongly preferred; oncology a plus. Expert-level Python + strong software engineering practices. Deep learning experience with PyTorch or TensorFlow (LLMs and/or CV). Data engineering: PySpark, SQL, Postgres, data modeling, query tuning. Cloud data platforms (Databricks, S3/Snowflake/Azure/GCP). Experiment design, statistical validation, and error analysis. HITL lifecycle design and feedback integration.

AI is essential to scaling the business—impact is immediate and visible. Opportunity to build an end-to-end applied AI stack powering clinical teams. Work across LLMs, CV, and multimodal use cases with strong platform engineering partners.

A fast-growing oncology-focused organization is reinventing how clinical trials operate by integrating them tightly with real-world clinical practice. Cross-disciplinary teams across healthcare, engineering, AI, and regulatory domains work in a Human-in-the-Loop (HITL) model to deliver regulatory-grade outcomes that expand trial access and accelerate cancer drug development.