Artificial Intelligence Engineer

Requirements Must have We are looking for an MSc or PhD in Computer Science, Electrical Engineering, Applied Mathematics, Statistics, Physics, or equivalent expertise gained through experience. We need 2–5+ years of experience in AI/ML engineering or applied data science. We strongly prefer experience in healthcare, clinical workflows, or oncology‑related settings. We expect advanced Python skills and strong software engineering discipline. We require hands‑on deep learning experience with PyTorch or TensorFlow, particularly for LLM or computer vision work. We need solid data engineering capabilities, including PySpark, SQL, Postgres, data modeling, and query optimization. We value experience with cloud data platforms such as Databricks, S3, Snowflake, Azure, or GCP. We expect a background in experiment design, statistical validation, and error analysis. We need familiarity with human‑in‑the‑loop system design and feedback integration. It is a plus if you also bring knowledge of R, Java, or C++, as well as MLOps practices such as versioning, lineage, and CI/CD. Responsibilities We design and deliver applied AI systems across LLMs, computer vision, and multimodal use cases to support clinical workflows. We build models and data pipelines for EMR/EHR data, imaging, and clinical documents. We translate unclear clinical needs into measurable machine learning objectives. We define success metrics, run experiments, and perform error estimation and analysis. We develop and improve human‑in‑the‑loop workflows to reduce manual quality assurance and increase throughput. We collaborate closely with platform and data engineering partners to ensure scalability, observability, and production readiness. We maintain high standards for code quality, reproducibility, experiment tracking, and technical documentation. We create and uphold documentation for datasets, experiments, model cards, and QA audits. We lead interim QA review processes and help evolve them toward AI‑assisted quality checks. We establish efficient patterns for working with PySpark, SQL, and Postgres to speed up iteration. We support HIPAA‑aligned safeguards and responsible AI practices throughout the development lifecycle. We contribute to productivity gains for internal abstraction and labeling teams by building automation and workflow improvements. Company We are a fast‑growing oncology‑focused organization rethinking how clinical trials are conducted by embedding them directly into clinical practice. Our cross‑functional teams bring together healthcare, engineering, AI, and regulatory expertise in a human‑in‑the‑loop model to produce regulatory‑grade outcomes that expand trial access and accelerate cancer drug development. We offer the opportunity to build an end‑to‑end applied AI stack with immediate impact, working across LLMs, computer vision, multimodal systems, and clinical data infrastructure. Our environment is mission‑driven, highly collaborative, and fast moving, with a strong emphasis on rigorous testing, validation, privacy, ethical AI, and scalable engineering practices. #J-18808-Ljbffr