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Tier-1 employer, metro location, and mid-level ML role increase applicant competition.
Core ML skills are transferable but life-sciences domain knowledge is preferred, yielding medium sensitivity.
Explicit 5–9 years requirement and specialized LLM/RAG/vector DB skills make filtering strict.
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Design, build, and integrate agentic AI capabilities that accelerate scientific discovery across domains like protein engineering and disease biology.
Develop reusable AI components and workflows combining foundation models, domain-specific models, retrieval systems, and scientific tools to support decision-making.
Collaborate closely with scientific domain leads and AI platform teams to translate research needs into scalable AI solutions and influence architectural direction.
BS or MS in Computer Science, Engineering, Computational Biology, Bioinformatics, or related field.
Strong hands-on experience developing AI and machine learning solutions with expertise in Python and modern AI/ML frameworks.
Experience with large language models (LLMs), agent frameworks, retrieval-augmented generation (RAG), vector databases, and API-driven architectures.
Work Experience Required: Bachelor's with 5–9 years of relevant experience.
Senior-level technical contributor skilled in agentic AI system design, scientific AI model integration, and workflow engineering for scientific domains.
Experience working closely with scientific researchers in collaborative, cross-functional environments to translate domain needs into technical AI solutions.
Capable of making independent implementation decisions on agent architectures, knowledge retrieval, model integration, and evaluation methods, influencing broader AI architectural direction.