AI Engineer I

Posted 14 Days Ago
South San Francisco, CA, USA
In-Office
100K-159K Annually
Junior
Biotech
The Role
Design, train, evaluate, and deploy multimodal and generative AI models and multi-agent systems for covalent small-molecule drug discovery. Build molecular representations (GNNs, equivariant nets), RAG pipelines, property-prediction and virtual-screening models, and production serving infrastructure to support medicinal chemistry and cross-functional discovery teams.
Summary Generated by Built In

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

What we are looking for:

  • 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.  

Traits we believe make a strong candidate: 


  • 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. 

Leveling Guidelines

Scientist I

  • 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 experts across disciplines and contribute to cross-functional team success


Benefits
  • Health Care Plan (Medical, Dental & Vision)
  • Retirement Plan (401k, IRA)
  • Life Insurance (Basic, Voluntary & AD&D)
  • Paid Time Off (Vacation, Sick & Public Holidays)
  • Family Leave (Maternity, Paternity)
  • Short Term & Long Term Disability
  • Training & Development
  • Free Food & Snacks
  • Wellness Resources
  • Stock Option Plan

At Frontier, we strive to build a diverse and equitable workplace. The salary range for this role is $100,000 - $158,900. Compensation for the role will depend on a number of factors, including candidates' qualifications, skills, competencies and experience. Frontier offers a competitive total rewards package which includes healthcare coverage, 401k and a broad range of other benefits.

This compensation and benefits information is based on Frontier's knowledge as of the date of publication, and may be modified in the future.

Skills Required

  • Bachelor's in computer science, data science, cheminformatics, computational chemistry or related field with 2-3 years industry experience (or PhD with ≥1 year industry experience)
  • Strong programming skills in Python
  • Experience designing architecture and training strategies for domain-specific multimodal AI models (molecular structures, assay data, protein structures, scientific text)
  • Familiarity with self-supervised learning, Mixture of Experts (MoE), and distributed training frameworks (DeepSpeed, FSDP)
  • Experience developing generative molecular design approaches (diffusion models, autoregressive transformers, VAEs, RL-guided generation)
  • Experience implementing and benchmarking molecular representations (GNNs, equivariant architectures, learned fingerprints) and multi-task/transfer/active learning approaches
  • Experience building multi-agent systems for scientific workflows, including orchestration, tool calling, error recovery, and human-in-the-loop checkpoints
  • Experience building RAG pipelines over structured data, slide decks, reports, and images
  • Experience training, evaluating, and deploying models for molecular property prediction, virtual screening, ADMET modeling, and SAR analysis
  • Experience deploying models into serving infrastructure (endpoints, batch inference, monitoring)
  • Ability to work independently and collaborate cross-functionally; excellent written and verbal communication skills
  • Legally authorized to work in the United States
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The Company
HQ: South San Francisco, CA
123 Employees
Year Founded: 2018

What We Do

Frontier Medicines, located in South San Francisco, CA and Boston, MA, is a precision medicine company that has pioneered a powerful discovery and development platform designed to generate medicines against disease-causing proteins previously considered undruggable. We are deploying our technologies in chemoproteomics, covalent drug discovery, and machine learning to develop a pipeline of groundbreaking medicines against the most important drivers of disease. Our lead program targets both the activated and inactive forms of KRAS G12C, a validated driver in a number of cancers that until recently has been undruggable. Our pipeline embodies our vision of bolding advancing science to defeat disease, starting with cancer.

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