About us
Founded in 2018, Causaly accelerates how humans acquire knowledge and develop insights in Biomedicine. Our production-grade generative AI platform for research insights and knowledge automation enables thousands of scientists to discover evidence from millions of academic publications, clinical trials, regulatory documents, patents and other data sources… in minutes.
We work with some of the world's largest biopharma companies and institutions on use cases spanning Drug Discovery, Safety and Competitive Intelligence. You can read more about how we accelerate knowledge acquisition and improve decision making in our blog posts here: Blog - Causaly
We are backed by top VCs including ICONIQ, Index Ventures, Pentech and Marathon.
About the role:
The ML Research Engineer will be a key addition to Causaly’s AI organisation. You will work alongside an interdisciplinary team of experts to develop and implement novel solutions to complex challenges with high levels of uncertainty.
Responsibilities
- Fine-tune and optimize large language models for specific tasks within biomedical research and drug discovery
- Design and implement intelligent agents capable of generating and testing scientific hypotheses, as well as interacting with the Causaly platform and external data sources
- Design and implement reinforcement learning algorithms to automate various aspects of drug discovery, including target identification and lead optimization
- Design, develop and maintain model training, evaluation, monitoring, dataset annotation and dataset management infrastructure
- Adopt a test-driven approach to produce a high-quality and efficient codebase, perform code reviews with other ML engineers to accept stories/deliverables
- Adopt an agile approach with quick iterations and adaptable solutions to meet the evolving needs of our product
- Document development milestones for a hybrid and multidisciplinary team
- Work closely with scientists to design large scale experiments to mature and productionize ML capabilities
- MSc/PhD in computer science, machine learning or equivalent
- Strong analytical and proven problem-solving skills
- Demonstrable industry experience delivering AI/ML frameworks for a product
- Expertise in working with ML frameworks such as PyTorch, Tensorflow, scikit-learn, Langchain
- Experience with DL architectures such as transformers/CNNs
- Excellent programming skills in Python and object-oriented paradigm
- Agile software development experience (comfortable with development management tools such as Jira, Rally)
- Excellent written and verbal communication skills
Nice-to-Have Skills and Background
- Applied RAG experience in industry
- Experience in Biomedical data or computational sciences
- Experience in building Reinforcement Learning frameworks
- Experience in cloud platforms such as GCP or AWS
- Experience with MLOps/LLMOps frameworks and best practices
0 applies
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