About the Opportunity
A global biopharmaceutical research organization is expanding its advanced AI research group to accelerate innovation in therapeutic discovery. The team focuses on applying cutting-edge machine learning methods throughout the end-to-end drug development lifecycle, combining computational modeling with biological and chemical sciences to improve how new therapies are identified and developed.
The organization is building a multidisciplinary environment where machine learning researchers, computational biologists, software engineers, and translational scientists collaborate to solve complex scientific problems using large-scale AI systems.
Position Summary
The company is seeking experienced AI Scientists to design, train, and deploy next-generation foundation models for biomedical and pharmaceutical research applications. This role involves developing advanced machine learning architectures capable of integrating diverse scientific data modalities, including genomic information, molecular structures, biological imaging, and protein-related datasets.
The successful candidate will contribute to both foundational model development and practical deployment across research platforms supporting therapeutic innovation.
Core Responsibilities
- Design and train large-scale AI systems for scientific and biomedical applications, including transformer-based architectures, generative models, and diffusion frameworks.
- Adapt and optimize existing pre-trained models for domain-specific use cases related to molecular discovery, disease understanding, and therapeutic target analysis.
- Develop multimodal learning systems capable of combining biological, chemical, imaging, and structured molecular data sources.
- Apply advanced machine learning techniques such as graph neural networks, protein language models, and generative modeling approaches to scientific research problems.
- Partner with interdisciplinary research teams to ensure AI systems are biologically and chemically meaningful.
- Build generative pipelines for molecular design, protein engineering, and optimization tasks involving multiple scientific constraints.
- Support deployment and scaling of AI capabilities across drug discovery workflows spanning small molecules, biologics, and emerging therapeutic modalities.
- Remain current with developments in machine learning, computational biology, and generative AI through research engagement and technical collaboration.
- Contribute to technical publications, internal innovation initiatives, and scientific knowledge sharing.
Required Qualifications
- PhD in Machine Learning, Computer Science, Computational Biology, Bioinformatics, Artificial Intelligence, or a related discipline; alternatively, equivalent industry experience with a master’s or bachelor’s degree.
- Strong expertise in deep learning methods, particularly transformer architectures, diffusion-based systems, and generative modeling.
- Hands-on experience training large-scale machine learning models using frameworks such as PyTorch.
- Experience with distributed computing environments and GPU-based model training infrastructure.
- Working knowledge of biological sciences, chemistry, or disease-related research sufficient to support scientifically informed model development.
- Background in at least one of the following:
- Protein language modeling
- Molecular generation systems
- Biomedical computer vision
Preferred Experience
- Industry experience within pharmaceuticals, biotechnology, or life sciences AI research.
- Familiarity with multimodal learning approaches integrating text, imaging, and molecular datasets.
- Experience working with genomics, transcriptomics, proteomics, or biological network data.
- Knowledge of protein structure modeling and 3D molecular representations.
- Publications in recognized machine learning or computational biology conferences and journals.
- Experience optimizing large models for efficient inference or production deployment.
- Strong Python programming skills and familiarity with ML libraries such as PyTorch, TensorFlow, or scikit-learn.
- Comfortable working within Unix/Linux-based research environments.
- Excellent communication and collaborative problem-solving abilities.
Location
This role is based in the Greater Boston/Cambridge biotechnology hub and is offered as a full-time position.
Compensation & Benefits
The organization offers a competitive compensation package that may include:
- Base salary aligned with experience and technical expertise
- Annual incentive opportunities
- Retirement savings plans with employer contributions
- Medical, dental, and vision coverage
- Paid vacation, sick leave, and company holidays
- Tuition assistance and professional development support
- Wellness and employee assistance programs
- Life and disability insurance coverage
- Paid volunteer or community engagement time
Equal Opportunity Commitment
The employer values diversity, inclusion, and equal opportunity in hiring and career development. All qualified applicants will receive consideration without regard to legally protected characteristics under applicable employment laws.
