Sr. Scientist I/Sr. Scientist II, Applied AI & Agentic Systems for Drug Discovery
Antaresrx · Boston, MA
About this role
**Department:** Discovery Predictive Sciences **Location:** Downtown Boston, MA (on-site / hybrid) **Salary Band:** $160,000–$220,000 **About Antares** Antares Therapeutics is a venture-backed precision medicine company advancing a pipeline of first-in-class small-molecule therapeutics for cancer and other serious diseases. We combine structural sciences, chemical biology, and data science to drug validated but previously intractable targets, moving our preclinical portfolio toward our first clinical trial. **Position Overview** We are seeking a scientist who builds. As part of a highly integrated team (medicinal chemists, chemical biologists, computational scientists, biologists, and machine learning scientists), you will develop and deploy **LLM-driven agents** for scientific workflows that shorten design–make–test–analyze cycles and sharpen program decisions. You will embed with project teams to understand the questions that gate progress—e.g., which compounds to make next, whether a target is tractable, and what assay and DMPK data are telling us. You’ll turn those needs into agentic systems that orchestrate tools and models to maximize the usage of our data (including retrieving relevant data, running analyses, checking outputs, and returning sourced answers). Multi-agent systems, **Model Context Protocol (MCP) servers**, and reusable skills for LLM-based tools are key enablers. This is a hands-on individual-contributor role on the Antares scientific track at the **Senior Scientist I** or **Senior Scientist II** level. **Key Responsibilities** - Embed with discovery project teams as the AI enablement partner; identify where agents can accelerate design–make–test–analyze cycles and influence program decisions. - Build and maintain **MCP servers** and tool integrations connecting LIMS/ELN, structural, assay, and DMPK data to agentic workflows—respecting data permissions so scientists can interrogate internal data directly. - Develop and deploy LLM-driven agents and multi-agent systems for scientific workflows (assay analysis, SAR interpretation, target assessments, DMPK understanding, and literature/competitive intelligence). - Own evaluation and benchmarking of deployed systems using sound MLOps practices: define task-specific benchmarks, run error analysis, and track accuracy/reliability for trustworthy outputs that support go/no-go decisions. - Develop reusable skills for LLM-based tools and reference implementations so solutions scale across the portfolio. - Analyze and integrate cross-functional data to independently define and address critical scientific questions; communicate approaches, limitations, and recommendations clearly to teams and leadership. - Lead AI enablement best practices on programs; evaluate and recommend platforms and emerging methods. **Qualifications** - PhD in computational chemistry or biology, cheminformatics, computer science, machine learning, or a related quantitative field (or equivalent experience). - **Senior Scientist I:** PhD with 5+ years (or bachelor’s with 13+ years) relevant experience. - **Senior Scientist II:** PhD with 8+ years (or bachelor’s with 16+ years), with demonstrated independent delivery and cross-functional influence. - Demonstrated direct contributions to **small-molecule drug discovery** programs in pharma/biotech, measurably influencing design decisions or program progression. - Hands-on development and deployment of LLM-driven agents
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