Scientist I, AI Engineer

Frontier Medicines•Boston, MA

5h•$134,840 - $158,900

About The Position

Frontier Medicines is seeking a highly motivated Scientist, AI Engineer to join our AI organization. This role will play a key scientific and technical role in advancing Frontier’s AI and cheminformatics capabilities in support of covalent small molecule drug discovery. The successful candidate will design and deploy machine learning models, agentic AI systems, and multimodal foundation and generative models that leverage Frontier's large-scale covalent chemistry and chemoproteomics data to accelerate compound design, prioritization, and optimization. This role works closely with medicinal chemistry, biology, and cross-functional drug discovery teams to build AI-powered tools that translate complex, heterogeneous data into actionable scientific insight This position is ideal for an individual contributor who thrives in scientifically uncharted territory and enjoys building novel solutions for medicinal chemistry, biology, and computational chemistry teams. This is an exciting opportunity in our South San Francisco site to deploy AI to make a difference for patients suffering from debilitating diseases by working in a highly collaborative and energetic team in a startup environment with short communication lines across functions and departments

Requirements

B.Sc in computational or quantitative discipline such as computer science, data science, cheminformatics, computational chemistry or related field with 2-3 years industry experience.
Strong programming skills in Python and experience with coding tools.
Ability to work independently while thriving in a highly collaborative, cross-functional environment.
Excellent written and verbal communication skills.
Legally authorized to work in the United States.
Bachelor’s degree with 2–3 years of relevant industry experience, or
PhD with at least 1 year of relevant industry experience
Demonstrated ability to independently or with limited guidance design and execute computational and AI approaches for drug discovery problems
Proven track record of contributing to complex analytical strategies and influencing discovery programs through data-driven insights
Ability to effectively collaborate with experts across disciplines and contribute to cross-functional team success

Responsibilities

Design architecture and training strategies for domain-specific multimodal AI models that reason over molecular structures, assay data, protein structures, and scientific text — trained on Frontier’s proprietary data. Familar with self-supervised learning, and Mixture of experts (MoE) and Distributed training (DeepSpeed, FSDP)
Design and build multi-agent systems for scientific research workflows — supervisor/sub-agent orchestration, MCPs, task decomposition, tool calling, error recovery, and human-in-the-loop checkpoints that interface with internal systems (scientific databases, compound registries)
Develop and adapt generative molecular design approaches — diffusion models, autoregressive transformers, VAEs, reinforcement learning–guided generation — to explore and expand covalent compound libraries and novel chemical space.
Build multimodal RAG pipelines over structured data, slide decks, experimental reports, and images
Train, evaluate, and deploy deep learning models for molecular property prediction, virtual screening, ADMET modeling, and SAR analysis on Frontier’s covalent chemistry data.
Implement and benchmark molecular representations — GNNs, equivariant architectures, learned fingerprints — and build multi-task,transfer learning, and active learning approaches that leverage proprietary data for low-data regime predictions on active programs.
Deploy models into serving infrastructure that researchers and agents can query — endpoints, batch inference, monitoring — so predictions reach decisions.