Senior Agentic Workflow Engineer, Biologics Discovery

Senior Agentic Workflow Engineer — build and maintain agentic workflows and orchestration for biologics discovery data analysis and secondary processing logic.

Key Responsibilities

• Build and continuously improve agentic workflows to automate secondary analysis for biologics discovery assays.
• Develop and maintain real-time or near-real-time pipelines connecting automation software, data stores, models, and compute.
• Partner with IT/platform teams to implement resilient APIs, observability, versioning, and workflow orchestration end-to-end.
• Extend shared GenAI platforms/models/agentic frameworks for biologics discovery (don’t duplicate enterprise capabilities).
• Work with ontology and MLOps colleagues so workflows produce semantically consistent, reusable data and deploy reliably to production.
• Contribute to AI-driven learning/closed-loop feedback across the DMTL (design-make-test-learn) cycle; improve downstream property models and decisions.
• Ensure outputs are high-quality, traceable, interoperable, AI-ready (metadata, provenance, lineage); propose opportunities to improve cycle time and reproducibility.

Required:

• Master’s or Ph.D. in CS/Engineering or related computational field (or Bachelor’s with substantial relevant experience).
• 2+ years building scientific workflow orchestration/automation, including agentic workflows.
• Hands-on real-time/near-real-time data pipeline design; integrate complex/heterogeneous scientific and instrument outputs.
• Experience applying agentic AI and LLM-based agents (or comparable intelligent automation) to scientific/lab workflows.
• Experience with enterprise/shared GenAI platforms and retrieval-augmented approaches (e.g., RAG/GraphRAG).
• Strong communication; comfort operating in ambiguity.

Preferred:

• Drug discovery domain experience (biologics, high-throughput experimentation, imaging).
• Lab automation/robotics/cyber-physical systems exposure.
• MLOps/Dev Ops, workflow engines, production monitoring/observability.
• FAIR data, ontologies/semantic models, lineage/provenance, AI-ready data standards.

• Base pay range: $109,000–$174,800; annual performance bonus eligibility.
• Medical, dental, vision, life insurance, short- and long-term disability, business accident insurance, group legal; retirement and 401(k).
• Vacation up to 120 hours/year; sick time up to 40 hours/year; holiday pay/floating holidays up to 13 days/year and work/personal/family time up to 40 hours/year.

Spring House, PA (strongly preferred), Titusville/Raritan, NJ, or Cambridge, MA, USA; or Madrid, Spain. No remote option.