Director, Research AI
Alnylam Pharmaceuticals
September 02, 2026
Remote friendly (Cambridge, MA)
United States
IT
Responsibilities:
- Partner with Research teams to embed AI-driven solutions into day-to-day scientific workflows.
- Identify high-value opportunities to augment target/ligand discovery, experimental design, data analysis, and decision-making.
- Drive adoption and measurable impact with assay teams and scientific leadership.
- Architect and deploy multi-agent systems for: Target Discovery (human genetics), Ligand Discovery (siRNA delivery to novel tissues/cell types), competitive intelligence (landscape/literature/target scouting), experimental design & hypothesis generation, data analysis & interpretation, and knowledge synthesis (public/in-house data), with coding/research agents and evaluation frameworks.
- Ensure systems are validated and continuously improved via lab feedback loops.
- Define and execute the AI roadmap aligned to Data & AI strategy; prioritize use cases with clear scientific/operational impact.
- Recruit and lead a 3–5 person interdisciplinary AI engineering & science team; provide hands-on technical leadership for AI/ML, agent development, and production deployment.
- Partner with IT to integrate AI into enterprise data/lakehouse; build secure, scalable, compliant systems across AWS and GCP; align with data governance/security/regulatory standards.
- Oversee production-grade pipelines, deployment frameworks, evaluation systems, and MLOps best practices.
- Provide computational/infrastructure support for ligand and antibody design; support AHG human genetics analyses.
Qualifications:
- PhD (or MS with equivalent experience) in Computational Biology, Computer Science, AI/ML, or related.
- 10+ years applying AI/ML in industry (preferably drug discovery).
- Proven leadership delivering AI platforms/products.
Skills/Experience:
- Agentic or multi-agent AI systems and workflow automation.
- Python; full-stack development preferred.
- Production-grade ML/AI development.
- AWS (including Bedrock) and/or GCP.
- Modern LLM ecosystems and large-scale data integration.
Preferred:
- Lab-in-the-loop AI systems.
- Enterprise/regulatory environments.
- Track record deploying AI cross-functionally.
- Biopharma/drug discovery experience; understanding experimental workflows; familiarity with nucleic acid therapeutics.
- Partner with Research teams to embed AI-driven solutions into day-to-day scientific workflows.
- Identify high-value opportunities to augment target/ligand discovery, experimental design, data analysis, and decision-making.
- Drive adoption and measurable impact with assay teams and scientific leadership.
- Architect and deploy multi-agent systems for: Target Discovery (human genetics), Ligand Discovery (siRNA delivery to novel tissues/cell types), competitive intelligence (landscape/literature/target scouting), experimental design & hypothesis generation, data analysis & interpretation, and knowledge synthesis (public/in-house data), with coding/research agents and evaluation frameworks.
- Ensure systems are validated and continuously improved via lab feedback loops.
- Define and execute the AI roadmap aligned to Data & AI strategy; prioritize use cases with clear scientific/operational impact.
- Recruit and lead a 3–5 person interdisciplinary AI engineering & science team; provide hands-on technical leadership for AI/ML, agent development, and production deployment.
- Partner with IT to integrate AI into enterprise data/lakehouse; build secure, scalable, compliant systems across AWS and GCP; align with data governance/security/regulatory standards.
- Oversee production-grade pipelines, deployment frameworks, evaluation systems, and MLOps best practices.
- Provide computational/infrastructure support for ligand and antibody design; support AHG human genetics analyses.
Qualifications:
- PhD (or MS with equivalent experience) in Computational Biology, Computer Science, AI/ML, or related.
- 10+ years applying AI/ML in industry (preferably drug discovery).
- Proven leadership delivering AI platforms/products.
Skills/Experience:
- Agentic or multi-agent AI systems and workflow automation.
- Python; full-stack development preferred.
- Production-grade ML/AI development.
- AWS (including Bedrock) and/or GCP.
- Modern LLM ecosystems and large-scale data integration.
Preferred:
- Lab-in-the-loop AI systems.
- Enterprise/regulatory environments.
- Track record deploying AI cross-functionally.
- Biopharma/drug discovery experience; understanding experimental workflows; familiarity with nucleic acid therapeutics.