Forward Deployed Engineer (Life Sciences)

Quick Facts

• Role: Forward Deployed Engineer (Life Sciences)

• Focus: End-to-end production deployments of AI models across drug discovery and clinical development

Description

You will lead end-to-end deployments of AI models inside life sciences organizations and research institutions, translating real-world data, infrastructure, and constraints into production systems. Success is measured through production adoption, measurable workflow impact, and evaluation-driven feedback loops with acceptance criteria that inform product and model roadmaps. You will work in regulated contexts with audit trails and traceable evidence, collaborating with Product, Research, Partnerships, GRC, Security, and GTM.

Responsibilities

• Design and ship production systems around models; own integrations, data provenance, reliability, and on-call readiness across research and operational workflows.

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

• Define and enforce launch criteria for regulated contexts, including validation evidence, audit readiness, and outcome metrics; drive delivery until sustained production impact is demonstrated.

• Build systems in sensitive scientific data environments where auditability, validation, and access controls shape architecture, operating procedures, and failure handling.

• Run evaluation loops that assess model/system quality against workflow-specific scientific benchmarks; use results to drive model and product changes.

• Distill deployment learnings into hardened primitives, reference architectures, validation templates, and benchmark harnesses that scale across regulated life sciences environments.

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 from scoping through production adoption; improved them using evaluation design, error analysis, and iterative evidence generation

• Delivered AI systems in trial design, regulatory writing, or scientific operations where validation strategy, auditability, compliance constraints, and reviewer expectations shaped system design and rollout

• Clear communication across scientific, clinical, technical, and executive audiences

• Systems thinking with strong execution; ability to turn failures, escalations, and audit findings into improved standards and repeatable deployment playbooks

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

Benefits

• Hybrid work model (3 days in office per week)

• Relocation assistance

• Travel up to 50%