Join our team to develop and validate advanced AI/ML models addressing complex challenges in life science R&D areas such as target choice, patient identification, molecule design and clinical trial effectiveness. Design and implement AI/ML pipelines for rapid experimental iteration, including classical ML models and advanced LLM customization techniques. Collaborate with subject matter experts and AI engineers to develop and deploy models and ensure high-quality, scientifically sound solutions.
Responsibilities:
Develop and validate advanced AI/ML models to tackle complex problems in target choice, patient identification, molecule design/chemistry, manufacturing and controls (CMC), and clinical trial effectiveness.
Design and implement AI/ML pipelines for data preprocessing, feature engineering, model training, hyperparameter tuning, and model evaluation, enabling rapid experimental iteration and adhering to industry’s best practices in MLOps.
Besides classical ML models fine-tuning (i.e., support vector machine and random forest), this team is also responsible for large language model (LLM) customization and fine-tuning using complex techniques (i.e., low-rank adaptation (LoRA) and reinforcement learning (RL) with human feedback).
Collaborate with AI engineers to deploy AI/ML models in both classical inference pipelines and agentic framework approaches.
Collaborate with subject matter experts in pre-clinical research, clinical trial design and operation, precision medicine, regulatory science, and CMC to guarantee scientifically sound and high-quality simulation modeling and analytical solutions.
Basic Qualifications:
BS degree in computer science, bioinformatics, applied math, statistics or engineering
4+ years of data science and machine learning developer experience
Experience working with LLM technologies, including developing generative and embedding techniques, modern model architectures, retrieval-augmented generation (RAG), fine tuning / pre-training LLM (including parameter efficient fine-tuning), and evaluation benchmarks.
Experience in data wrangling from databases for feature engineering and model training purposes.
Experience in Python, TensorFlow/PyTorch, and scalable ML architectures.
Experience with AI/ML model metrics (e.g., F1 and AI-contents evaluation metrics) including setting up human-in-the-loop (HITL) AI/ML monitoring.
Coding and software engineering skills, and knowledge with software engineering principles around testing, code reviews and deployment.
Preferred Qualifications:
MA degree in computer science or equivalent qualitative science fields
Experience with reinforcement learning (RL) and multi-agent framework
Experience with graph database in the context of GraphRAG
Experience with computer vision
Experience designing and managing AI workloads on cloud platforms and/or high-performance computing environments
Knowledge of cost optimization strategies for GPU computing in both cloud and on-premises scenarios
Proficiency with distributed computing frameworks (i.e., Spark, databricks, RAPIDS.ai)
Experience in establishing AI/ML best practices, standards, and ethics
Experience in AI/ML applications in life science domain areas: pre-clinical research, clinical trial design and operation, precision medicine, regulatory science, and CMC.
Strong written and verbal communication skills
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GSK is a global biopharma company with a special purpose – to unite science, technology and talent to get ahead of disease together – so we can positively impact the health of billions of people and deliver stronger, more sustainable shareholder returns – as an organisation where people can thrive. We prevent and treat disease with vaccines, specialty and general medicines. We focus on the science of the immune system and the use of new platform and data technologies, investing in four core therapeutic areas (infectious diseases, HIV, respiratory/ immunology and oncology).
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