Forward Deployed Engineer (FDE) - Life Sciences

Quick Facts

• Role: Forward Deployed Engineer (FDE) supporting life sciences deployments

• Focus: End-to-end production deployment of AI systems across drug discovery and development workflows

• Success measures: Production adoption, measurable workflow impact, and evaluation-driven feedback loops

Description

You will lead deployment of production-grade AI systems inside life sciences organizations, partnering with domain experts to translate real-world constraints into systems that can be adopted in regulated environments. You’ll run eval loops and define launch criteria using workflow-specific benchmarks and validation evidence, ensuring audit readiness and traceable documentation. You’ll also distill deployment learnings into reusable primitives and reference architectures.

Responsibilities

• Design and ship production systems around models, owning integrations, data provenance, reliability, and on-call readiness across research and clinical/operational workflows

• Lead discovery and scoping from pre-sales through post-sales; translate ambiguous needs into hypothesis-driven problem framing, requirements, and measurable execution plans

• Define and enforce regulated-context launch criteria, including validation evidence, audit readiness, and outcome metrics

• Build in sensitive scientific data environments where auditability, validation, and access controls shape system architecture and failure handling

• Run evaluation loops against workflow-specific scientific benchmarks; use results to drive model and product changes

• Convert deployment learnings into hardened primitives, reference architectures, validation templates, and benchmark harnesses

Requirements

• 5+ years of software/ML engineering or technical deployment experience with customer-facing ownership in biotech, pharma, clinical research, or scientific software

• Owned customer GenAI deployments end-to-end (scoping through production adoption) and improved them via evaluation design, error analysis, and iterative evidence generation

• Delivered AI systems where validation strategy, auditability, compliance constraints, and reviewer expectations shaped design and rollout (e.g., trial design, regulatory writing, scientific operations)

• Clear communication across scientific, clinical, model research, technical, and executive audiences, translating tradeoffs into decision quality and measurable outcomes

• Strong systems thinking and execution, turning failures/escalations/audit findings into improved operating standards and repeatable playbooks

• PhD, MS, or equivalent applied experience in a life sciences relevant field encouraged

Benefits

• Hybrid work model: 3 days in the office per week

• Relocation assistance

• Travel up to 50%