Sr. Scientist I/Sr. Scientist II, Applied AI & Agentic Systems for Drug Discovery

Antares Therapeutics; Boston (MA); Hybrid; USD 160,000 - 220,000; Full time

Job summary

Antares Therapeutics is seeking a Senior Scientist I/II to build AI-enabled workflows for drug discovery in Downtown Boston. You will develop and deploy multi-agent systems and MCP servers, integrating data across chemistry, biology, and computation to accelerate design-make-test-analyze cycles.

You will lead AI enablement efforts, collaborate across disciplines, and ensure reliable, benchmarked ML practices that inform program decisions in a lean discovery environment.

Qualifications

• PhD in computational chemistry, biology, cheminformatics, CS, ML, or related field, or equivalent experience.
• Senior Scientist I: PhD with 5+ years (or bachelor with 13+ years) of relevant experience.
• Senior Scientist II: PhD with 8+ years (or bachelor with 16+ years) and demonstrated independent impact.
• Hands-on development of LLM-driven agents and multi-agent systems for scientific workflows.

Responsibilities

• Embed with discovery project teams as AI enablement partner across chemistry, biology and ML.
• Build and maintain MCP servers and tool integrations connecting data to workflows.
• Develop and deploy LLM-driven agents and multi-agent systems for scientific workflows.
• Own evaluation, benchmarking, and MLOps practices to ensure trustworthy outputs.
• Develop reusable skills for LLM-based tools and reference implementations.
• Analyze data across functions and communicate findings to leadership.

Skills

Python programming

LLM agents

MLOps

Cross-functional collaboration

Strong communication

Education

PhD in computational chemistry or biology

Bachelor's degree + extensive experience

Tools

Claude

Codex

RDKit

OpenEye

Schrödinger

Job description

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.
• Develop and deploy LLM-driven agents and multi-agent systems for scientific workflows, orchestrating tools and models to, for example, enable assay data analysis, SAR interpretation, target assessments, DMPK understanding, and literature and competitive intelligence.
• Own evaluation and benchmarking of what you deploy, supported by sound MLOps practices — defining task-specific benchmarks, running error analysis, and tracking accuracy and reliability so outputs are trustworthy enough to inform go / no-go decisions.
• Develop reusable skills for LLM-based tools and reference implementations, so a solution built for one program serves the whole portfolio.
• Analyze and integrate data across functions to independently identify, define, and address critical scientific questions; communicate approaches, limitations, and recommendations clearly to project teams and leadership, and help colleagues use these tools confidently.
• Act as the AI enablement function lead on programs, establish internal best practice, and 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) of relevant experience.
• Senior Scientist II: PhD with 8+ years (or bachelor's with 16+ years), with a demonstrated record of independent delivery and cross-functional influence.
• Demonstrated, direct contributions to small-molecule drug discovery programs in a pharmaceutical or biotechnology setting — where your work measurably influenced design decisions or program progression. This is central to the role.
• Hands-on development and deployment of LLM-driven agents and multi-agent systems for scientific workflows, including MCP servers and tool integrations, using current agent frameworks and model-provider SDKs with frontier models (e.g., Claude, GPT). We are framework-agnostic: what you build matters more than what you built it with.
• Strong programming in Python, with fluent, effective use of coding agents such as Claude Code or Codex.
• Experience with model evaluation and benchmarking, and with MLOps practice for reliable, reproducible deployment.
• Working knowledge of medicinal chemistry, SAR, structural biology, and DMPK principles sufficient to be a credible partner to drug hunters; familiarity with cheminformatics toolkits (e.g., RDKit, OpenEye, Schrödinger) is strongly preferred.
• Excellent written and verbal communication skills, with a proven ability to drive adoption among scientific colleagues and to make complex technology approachable.
• A passionate, results-oriented drug hunter who thrives in a collaborative, data-driven, and scientifically ambitious culture, and who measures success by program impact rather than tools shipped.

The anticipated annual salary range for this position is $160,000–$220,000. The role is also eligible for an annual target bonus and equity participation commensurate with level. The actual salary offered will be based on a number of factors, including but not limited to the qualifications of the applicant, years of relevant experience, level of education attained, certifications or other professional licenses held, and, if applicable, the location in which the applicant lives and/or from which they will be performing the job.

Antares Therapeutics is an equal opportunity employer. As set forth in our Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.