Key skills
PythonSoftware EngineeringDeep LearningPyTorchTensorFlowLLM DevelopmentComputer VisionMultimodal AIData EngineeringPySparkSQLPostgresData ModelingQuery OptimizationCloud Data PlatformsExperiment DesignStatistical ValidationError AnalysisHuman-in-the-Loop (HITL) Lifecycle DesignJavaC++CRAIMLLLMMLOpsSnowflakeDatabricksAzureGCPS3CI/CDCollaboration
About this role
Harnham is a fast-growing oncology-focused organization reinventing how clinical trials operate by integrating them tightly with real-world clinical practice. The AI Engineer will design and deliver applied AI systems that automate clinical variable extraction and clinical note generation, focusing on LLM development and collaboration with platform engineering to ensure model usability and efficiency.
Responsibilities:
- Build AI models and pipelines across EMR/EHR, imaging, and clinical documents
- Translate ambiguous clinical requirements into measurable ML objectives
- Define metrics, design experiments, and estimate/model error
- Lead interim QA audit processes and evolve toward AI-assisted QA
- Partner with data/platform engineers on scalability, data flow, and observability
- Champion code quality, experiment tracking, reproducibility, and knowledge capture
Requirements:
- 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
- Additional languages: R, Java, C++
- MLOps fundamentals (versioning, lineage, CI/CD)
- Prior oncology or clinical trials exposure