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 world-class structural sciences, chemical biology, and data science to drug validated but previously intractable targets. We are advancing our preclinical portfolio towards our first clinical trial. With teams in Boston and South San Francisco, we are unlocking the possibilities of science and technology to accelerate targeted therapies for a brighter human future.
Position Overview
We are seeking a scientist who builds. As a member of a highly integrated team of medicinal chemists, chemical biologists, computational scientists, biologists, and machine learning scientists, you will develop and deploy LLM-driven agents for scientific workflows that shorten our design-make-test-analyze cycles and sharpen program decisions.
You will sit with project teams and understand the questions that actually gate progress: which compounds to make next, whether a target is tractable, what the assay and DMPK data are telling us. You will turn them into agentic systems that reliably orchestrate our tools and models to maximize the usage of our data. In practice that means software that carries out multi-step work on a scientist’s behalf: retrieving the 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 the means; better and faster discovery decisions are the end.
This is a hands-on individual-contributor role on the Antares scientific track, for someone who has already contributed to drug discovery programs and wants to multiply what a lean discovery organization can do. The role is offered at the Senior Scientist I or Senior Scientist II level; scope, independence, and cross-functional influence scale with experience, as described under Qualifications.
Key Responsibilities
- Embed with discovery project teams as the AI enablement partner, and work alongside medicinal and computational chemists, chemical biologists, and machine learning scientists to identify where agents can accelerate design-make-test-analyze cycles and influence program decisions.
- Build and maintain MCP servers and tool integrations that connect our LIMS/ELN, structural, assay, and DMPK data to agentic workflows, respecting existing data permissions, so scientists can interrogate our own data directly.
