Senior Machine Learning Engineer AI / RL

Job description :

Our partner is a pioneer in AI-driven drug discovery and development, harnessing cutting-edge generative AI and deep learning technologies to accelerate the creation of novel therapeutics. Our Pharma.AI platform combines biology, chemistry, and clinical data to transform the drug discovery process from target identification and molecule generation to clinical trial optimization.

Join the team and help push the boundaries of AI in healthcare, bringing life-saving treatments to patients faster.

We are seeking a motivated Machine Learning Engineer / AI Developer to develop and optimize large language models (LLMs) for our Pharma.AI platform. This role focuses on building AI solutions tailored to the needs of biopharmaceutical partners, enabling faster drug discovery and collaborative R&D. You will work closely with cross-functional teams of AI researchers, scientists, and software engineers to deploy scalable, industry-leading AI tools.

Key Responsibilities :

• Design, train, and fine-tune LLMs for biomedical text analysis, knowledge extraction, and multi-modal data integration.
• Collaborate with domain experts to translate pharma R&D challenges into AI-driven solutions (e.g., literature mining, target validation, predictive modeling).
• Stay updated on SOTA LLM architectures (e.g., Transformer variants, retrieval-augmented models) and adapt them for biomedical use cases.
• Optimize model performance for deployment in B2B environments, ensuring scalability, latency, and regulatory compliance.
• Write clean, maintainable code and contribute to ML pipelines.
• Participate in code reviews, model validation, and documentation.
• Support the integration of LLMs into end-to-end Pharma.AI platform.

Skills and qualifications :

Technical Skills : Strong understanding of LLM architectures (Transformer, BERT, GPT, etc.) and NLP techniques (tokenization, embeddings, attention mechanisms).

Proficiency in Python and ML frameworks (PyTorch, TensorFlow, JAX).

Experience with distributed training, model optimization, or LLM deployment is a plus.

Domain Knowledge : Prior exposure to biomedical / healthcare data (e.g., scientific literature, clinical trials, omics) is highly preferred.

Familiarity with B2B AI product development cycles (requirement gathering, prototyping, enterprise deployment) is a strong advantage.

Preferred Qualifications Publications or projects in NLP / LLMs applied to life sciences.

Experience with cloud platforms (AWS, GCP) and containerization (Docker, Kubernetes).

Contributions to open-source ML projects.