Related skills
python machine learning genai production systems data provenance๐ 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.
๐ฏ 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.
๐ Benefits
- Salary: USD 220Kโ280K per year
- Equity included
- Hybrid work model: 3 days in the office per week
- Relocation assistance
- Travel up to 50% of the time
๐ Relocation support
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