Senior Developer Relations Lead, AI-Enabled Drug Discovery Science

Quick Facts

• Role: Lead AI-enabled drug discovery science and coordinate science streams across experimental, computational, automation, and translational activities.

Description

You will lead workstreams spanning data curation & analysis, machine learning, automation, and platform engineering to advance AI-ready therapeutic discovery programs at the intersection of RNA therapeutics and computational modeling. You will translate therapeutic objectives into executable operating plans, guide closed-loop learning from AI-generated hypotheses through automated experiments and model updates, and ensure scientific validity through governance, quality thresholds, and risk/dependency tracking.

Responsibilities

• Coordinate the operating plan across research priorities, execution, and program achievements.

• Convert scientific therapeutic objectives into executable plans.

• Lead workstreams across data curation & analysis, machine learning, automation, and platform engineering.

• Collaborate on assay and readout strategies for efficacy, selectivity, adaptability, translatability, and safety data readiness.

• Guide closed-loop learning across AI-generated hypotheses, automated experiments, multiplexed readouts, model updates, and science decisions.

• Review science validity for key biology applications.

• Establish science-stream governance: build reviews, decision logs, risk/dependency tracking, quality thresholds, and paths to unblock issues.

• Provide workstream accounting for program leadership, covering progress, critical decisions, cross-team dependencies, resource needs, and unresolved risks.

Requirements

• PhD experience in research within life sciences, therapeutic discovery, chemical biology, molecular pharmacology, computational biology, bioengineering, or a related field, or equivalent experience.

• 12+ years of research or drug-discovery experience in therapeutic discovery and platform biology.

• Deep understanding of therapeutic development and translational biology, including target identification, experimental context, efficacy/selectivity tradeoffs, safety considerations, and data quality.

• Experience leading large interdisciplinary teams across experimental science, computational modeling, automation, assay development, data analysis, data platforms, and with external partners.

• Proven track record leading in matrixed environments with shared scientific direction, program priorities, and execution accountability.

• Proficiency with high-content and high-throughput data generation, including molecular, cellular, and functional readouts, assay quality control, label definition, experimental composition, and model validation.

• Ability to collaborate deeply with AI and infrastructure teams on model requirements, feature stores, data lineage, compute planning, and evaluation metrics for closed-loop experimentation.

Benefits

• Base salary range varies by level, location, and experience.

• Eligible for equity and benefits.