Senior Lead AI Engineer
(AI Foundations and Agentic AI)

About the Company

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Medicas AI’s mission is to shift the AI paradigm and make personalized medicine / healthcare options accessible to everyone.  Co-Founded by renowned AI expert, Professor Heng Ji at UIUC, Medicas AI is building an AI engine that can think like a scientist.

 

Are you a high-performing, motivated engineer ready to make a significant impact in the AI for drug discovery space? Medicas AI is seeking talented Lead AI engineers to join our founding engineering team. We are a fast-paced, customer focused company with huge market potential. If you thrive in an environment that values hard work, collaboration, and technical curiosity, and want to be part of the early founding engineer team to make a disruptive AI product for life science, we would like to hear from you.

 

In this role, you will:

 

• Design, develop, test, deploy, and support Medicas AI’s 1st commercial LLM platform for drug discovery, including foundation model training, large language model inference, model evaluation, experimentation, governance, model inference, system deployment, and observability, etc.

• Contribute to the technical vision and the long-term roadmap of foundational AI systems for Medicas AI’s product portfolio.

• Collaborate Across Teams: Work closely with internal Chemistry team, and external customers’ R&D team to ensure that technical solutions align with product goals.

 

Required Qualifications

 

• Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 2 years of experience developing ML or LLM-related algorithms or technologies.

• Ability to demonstrate technical skills via a public GitHub profile, code samples, or a take-home challenge.

• Ability to clearly explain and implement recent state-of-the-art LLM and LMM architectures， post-training algorithms and agentic workflows.

• Proficiency in Python and ML frameworks, with a focus on hands-on experience with training foundation models (preferably multi-model foundation models), familiarity with foundation model architecture modifications, developing agentic or reasoning frameworks, reinforcement learning, and/or data-efficient training.

• Expertise in model inference and deployment frameworks, including optimizing and scaling LLM systems in production environments.

• Strong communication and learning skills and a collaborative mindset, with the ability to work effectively in cross-functional teams and to learn and communicate across stacks.

 

Preferred Qualifications:

 

• Familiarity with large-scale pre-training and post-training infrastructure, including experience designing and operating distributed training pipelines across multi-node GPU clusters; hands-on experience with frameworks such as Megatron-LM, DeepSpeed, FSDP, vLLM, and VERL, as well as cluster orchestration, monitoring, and high-throughput data pipelines.

• Preferably with experience working with other ML algorithms, including but not limited to GNNs, diffusion models, etc.).

• Background in biology, chemistry, pharma, and/or pharmacy is a strong plus, such as Biology or Chemistry Olympiads experiences or a minor degree in related fields.