AI/Foundation Model Architect
echemlabsUnion (NJ)
AI/Foundation Model Architect
Posted yesterday
echemlabsUnion (NJ)
SENIORITY
Lead
About the role
eChemLabs is looking for a talented AI/Foundation Model architect to join its product team. This is a unique opportunity to join a high-performing team and an industry-leading product whose customers are at the leading edge of scientific innovation. In this role, you will build the key technologies that further the mission of eChemLabs to help our customers develop electrochemical solutions faster! You will develop a novel Deep Learning architecture/Foundation Model to be used in the eChemLabs platform to create an embedded electrochemical solution space and to develop structured input/structured output solutions. This is a remote-first role based in the EU and requires less than 20% travel. As an AI/Foundation Model Architect, you will: Hands-on experience with fine-tuning or developing and evaluating Foundation Models Hands on experience on Deep Learning structured input/structured output solutions Strong background in Python and experience with prompt engineering, tools integration, and chaining logic. Solid understanding of Deep Learning LLM architecture, RAG, vector stores, tool use, and memory architectures. Ability to lead architectural discussions, PoCs, and hands-on development in fast-paced environments. Model-cost profiling and budgeting (API call minimization, batch vs. streaming) Obsessed with rigor and attention to detail.
Minimum Qualifications:
Must-Have Skills and Qualifications Master or PhD degree in Deep Learning required, with a specialization in development, improvement and evaluation of Foundation Model Ability to independently execute as a highly motivated individual in a fast-moving startup environment Expertise programming in Python and Deep Learning libraries in HPC environments Obsessed with rigor and attention to detail.
Preferred Qualifications:
Hands-on experience on fine-tuning/evaluation pipelines for Foundation Models Hands-on experience on developing Deep Learning structured input/structured output
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