AI Engineer

Frontier Medicines isseekinga highly motivated

Scientist,AIEngineerto join ourAIorganization. This role will play a key scientific and technical role in advancing Frontier’sAIand cheminformatics capabilities in support ofcovalentsmall 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 solutionsformedicinalchemistry, biology, and computational chemistry teams.

This is an exciting opportunity in our South San Francisco site to deployAIto 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

• Design architecture and training strategies for domain-specific multimodalAImodels that reasonovermolecular structures, assay data, protein structures, and scientific text — trained on Frontier’s proprietary data.
  
  Familarwithself-supervised learning, and Mixture of experts (MoE) and Distributed training (Deep Speed, 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 thatleverageproprietary datafor low-data regime predictions on active programs.

• Deploy models into serving infrastructure that researchers and agents can query — endpoints, batch inference, monitoring — so predictionsreachdecisions.

• B.Scin computational or quantitative discipline such as computer science, data science,cheminformatics, computationalchemistryorrelated field with 2-3 years industry experience.

• Strong programming skills in Pythonand 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.

• Education & Experience:
  • 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…