Key skills
AI/ML EngineeringPythonTensorFlowNVIDIA GPU environmentsLanguage model developmentModel evaluation methodologiesRegulated environments experienceAnalytical thinkingcommunicationTechnical documentationProblem-solvingRAIMLPyTorchMLOpsJAXMLflowKubeflowSageMaker
About this role
Planet Pharma is seeking an experienced AI Engineer with a strong background in pharma, biotech, or life sciences. The role involves supporting multiple AI initiatives across scientific and operational teams, focusing on document ranking models, small language models, and scalable GPU-based infrastructure.
Responsibilities:
- Develop and optimize document ranking models and retrieval pipelines
- Build and fine-tune small language models for scientific, regulatory, and operational use cases
- Perform model rebalancing, weight calibration, and accuracy optimization
- Create model evaluation dashboards and visualizations (including metric breakdowns and performance summaries)
- Train, optimize, and deploy models within NVIDIA GPU environments
- Design and maintain TensorFlow-based training and inference pipelines
- Write clean, scalable Python code for model development, automation, and data processing
- Integrate AI models into broader R&D and commercial systems
- Troubleshoot complex model performance and data quality issues
- Ensure compliance with pharma documentation, validation, and regulatory standards
Requirements:
- 4–7+ years of experience as an AI/ML Engineer
- Direct experience within pharmaceutical, biotech, or life sciences environments
- Strong hands-on expertise in: Python
- Strong hands-on expertise in: TensorFlow
- Strong hands-on expertise in: NVIDIA GPU compute environments (CUDA, cuDNN, etc.)
- Experience developing, rebalancing, and fine-tuning language models
- Background in ranking systems, retrieval pipelines, search relevance, or recommendation systems
- Strong understanding of model weighting, calibration, and evaluation methodologies
- Experience building and interpreting model performance metrics and visualizations
- Experience deploying models in regulated or GxP-aligned environments
- Experience with vector databases or semantic search platforms
- Familiarity with PyTorch or JAX
- Exposure to MLOps tools (MLflow, Kubeflow, SageMaker, etc.)
- Understanding of pharma data governance and validation frameworks