Seeking an AI‑driven Research Scientist to push the boundaries of immuno‑oncology and next‑gen biologic design.
Proclinical is seeking a Research Scientist specializing in AI and Machine Learning to support innovative advancements in immuno-oncology and pharmaceutical research.
Primary Responsibilities:
The successful candidate role will focus on integrating cutting-edge AI/ML technologies to accelerate discovery and optimization across biologic modalities, including antisense oligonucleotide (ASO) therapeutics and antibody design. You will also play a key role in developing computational frameworks and predictive models to enhance therapeutic design pipelines.
Skills & Requirements:
- PhD in Computational Chemistry/Biology, Machine Learning, Biomedical/Chemical Engineering, or a related field.
- Strong background in oligonucleotide chemistry and antibody design.
- Proven experience in computational modeling of antibody-antigen interactions.
- Expertise in probabilistic learning, deep learning models (e.g., RNNs, GNNs, Transformers), and generative AI.
- Proficiency in programming languages such as Python, R, and SQL, with hands-on experience in frameworks like PyTorch, TensorFlow, or JAX.
- Experience developing machine learning models for DNA, RNA, and proteins, including language models and structure prediction.
- Familiarity with large-scale computing, cloud infrastructures, and database systems.
- Knowledge of tools like AWS, GitHub/GitLab, and Docker containers.
- Strong communication skills to collaborate effectively with multidisciplinary teams.
- Commitment to teamwork and continuous learning.
The Research Scientist's responsibilities will be:
- Design and implement advanced AI/ML approaches for antibody discovery, including fine-tuning protein language models and generative protein design workflows.
- Develop scalable machine learning methods for multi-objective optimization of biologics such as antibodies, antigens, ADCs, and other modalities.
- Build sequence-aware predictive models to prioritize ASO designs based on exon-skipping responses across diverse targets and modalities.
- Create reproducible computational frameworks for biologics, encompassing data ingestion, feature engineering, model training, validation, and deployment.
- Curate and harmonize datasets, defining robust sequence and structure features to drive model performance.
- Establish benchmarks and collaborate with experimental teams to validate predictions.
- Evaluate and adopt tools to enhance modeling workflows and decision support systems.
- Maintain a clean, well-documented codebase and provide user guidance for cross-functional teams.
- Perform additional related tasks as assigned.
If you are having difficulty in applying or if you have any questions, please contact Mike Raletz at m.raletz@proclinical.com
If you are interested in applying to this exciting opportunity, then please click 'Apply' or to speak to one of our specialists please request a call back at the top of this page.
Proclinical is a leading life sciences recruiter focused on finding exceptional people and matching them with the finest positions across the globe. Proclinical is acting as an Employment Agency in relation to this vacancy.
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