Forward Deployed Engineer (FDE), Life Sciences - NYC

Added
2 minutes ago
Type
Full time
Salary
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Related skills

python machine learning genai production systems data provenance

๐Ÿ“‹ Description

  • Design and ship production systems around models, owning integrations, data provenance, reliability, and on-call readiness across research, clinical, and operational workflows.
  • Lead discovery and scoping from pre-sales through post-sales, translating ambiguous workflow needs into hypothesis-driven problem framing, system requirements, and an execution plan with measurable endpoints.
  • Define and enforce launch criteria for regulated contexts, including validation evidence, audit readiness, outcome metrics, and drive delivery until we demonstrate sustained production impact.
  • Build in sensitive scientific data environments where auditability, validation, and access controls shape architecture, operating procedures, and failure handling.
  • Run evaluation loops that measure model and system quality against workflow-specific scientific benchmarks and 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.

๐ŸŽฏ Requirements

  • Bring 5+ years of software/ML engineering or technical deployment experience with customer-facing ownership in biotech, pharma, clinical research, or scientific software; PhD, MS, or equivalent applied experience in a life sciences relevant field encouraged.
  • Have owned customer GenAI deployments end-to-end from scoping through production adoption, and improved them through evaluation design, error analysis, and iterative evidence generation that tightens acceptance criteria over time.
  • Have 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.
  • Communicate clearly across scientific, clinical, model research, technical, and executive audiences, translating technical tradeoffs into decision quality, risk posture, and measurable outcomes with credibility.
  • Apply systems thinking with high execution standards, consistently turning failures, escalations, and audit findings into improved operating standards, validation artifacts, and repeatable deployment playbooks.

๐ŸŽ Benefits

  • Salary: USD 220Kโ€“280K per year
  • Equity included
  • Hybrid work model: 3 days in the office per week
  • Relocation assistance
  • Travel up to 50% of the time

๐Ÿšš Relocation support

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