Ml Engineer - Large Language Models & Generative Ai

About the roleWe’re seeking an experienced Machine Learning Engineer to contribute to development of software and specifically LLM-based systems that will help in our mission of industrializing scientific discovery to radically improve lives. We're looking for individuals with strong engineering skills, including expertise in designing, implementing, improving, and deploying large language modeling systems at significant scale. In addition, we value proficiency with state‑of‑the‑art machine learning algorithms and exceptional problem‑solving skills. In this role, you will:Support Valence Labs’ research agenda for the application of LLM-based applications in drug discovery.Engage with and contribute to open‑source libraries developed by Valence and the research community.Create and improve novel ML methods applicable to LLM systems that will accelerate drug discovery.Collaborate with an interdisciplinary team of dry and wet lab scientists to inform and improve our models and systems.Fine‑tune existing open source LLMs and potentially develop proprietary models for specialized tasks.Design and implement a Retrieval‑Augmented Generation (RAG) system leveraging vector database technology to enhance the model’s capability for generating contextually relevant and accurate responses by efficiently retrieving and incorporating external data.Enhance system capabilities by researching advanced reasoning frameworks for orchestrating complex workflows, incorporating multi‑option decision‑making, self‑reflection, and external memory integration to improve planning, evaluation, and knowledge retrieval.A successful candidate will have most of the following:PhD or Master's degree with 3+ years of industry experience.Strong programming skills and understanding of modern software development practices, especially in Python.LLM application development experience, ideally with LangChain, and experience building RAG systems/Prompt Engineering or eagerness to learn is highly desirable.Proven track record in machine learning, including building LLM-based systems (ideally with LangChain library), designing new architectures, hands‑on experimentation, analysis, visualization, and model deployment in production systems.Demonstrated capability to understand and summarize scientific content and implement deep learning models and novel LLM methods based on descriptions from publications.Strong knowledge of linear algebra, calculus, and statistics.Passion for applying ML research to real‑world problems.Nice to have:Experience in building and deploying high‑performance implementations of NLP deep learning algorithms.Scientific knowledge of biology, chemistry, or physics along with previous experience working in a scientific environment across disciplines.Contribution to high‑visibility ML codebases.Valence Labs is committed to creating a diverse and inclusive environment, where understanding and accommodating personal needs and preferences is a priority. Join our multidisciplinary team of passionate researchers, eager to push the boundaries of ML research and contribute to industrializing scientific discovery to radically improve lives.
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