Principal Architect, Platform & Data Lake

What you'll own

Enterprise Platform: Tenancy, IAM, compliance and admin control plane that enterprise customers use to govern their scientific data environment: SSO/SAML/OIDC, fine-grained RBAC, multi-tenant isolation, UI infrastructure, and tenant onboarding.

Scientific Search: Search architecture spanning keyword, semantic, and hybrid retrieval across scientific data, instruments, and metadata: relevance standards, indexing pipeline, and the infrastructure that makes search a reliable product surface.

AI/ML Ops: Model serving, agentic infrastructure primitives, embedding services, and the MLOps standards that keep scientific AI outputs traceable and operable under production load.

Developer Platform: The internal paved road: CI/CD standards, golden path tooling, SDK design principles, and the adoption metrics that prove it works.

Developer Productivity: Developer throughput as a first-class metric: toolchain ownership, local/prod environment parity, and friction reduction from commit to deployment.

Lakehouse Platform: Scientific data lake architecture, schema evolution, IDS design standards, and the data access layer that AI workloads and downstream pipelines depend on.

Partner Integrations: Integration architecture for lab instrument vendors and AI model partners: reference patterns, security boundaries, and the developer experience that enables self-service onboarding.

Cloud Infrastructure: Production architecture, cost governance, and the observability layer from infra signal to customer-visible service health.

What success looks like in year one

• Authn/Authz architecture is documented, consistent across services, and passing enterprise security reviews without heroics from a single engineer.
• AI/ML infrastructure has a clear architecture and roadmap for MLE inference and training use cases, with strong operational telemetry and cost visibility.
• The developer platform has clear SDKs and a set of standard templates for scientific use cases to start from, with adoption and delivery by multiple scientific use case teams.
• Operational excellence based on a clear O11y architecture rolled out, with every production service having SLOs defined, monitored and managed.
• Cost governance with customer chargeback attribution architecture and operationalized with the finance and field teams.
• Lakehouse platform architecture and operational buildout as a Data Products Platform with strong DX and operational scaling.
• Evolve IDS to open standards based schema and encoding with strongly typed data models and schema-on-write enforcement.
• Published reference architecture for each partner class (lab instrument manufacturers and AI models), with one partner successfully onboarded against each without bespoke engineering support.

Requirements

• 12+ years in software engineering, with at least 5 at staff or principal level in a SaaS platform or data infrastructure context.
• Deep architecture ownership in at least one of the two fingerprint profiles (Enterprise Data & AI Platforms or Data, Knowledge, and Developer Products), with meaningful range across the other. Coverage of a majority of the eight domains is the bar.
• Demonstrated ownership of enterprise authentication and authorization systems at scale: SAML, OIDC, fine-grained RBAC across a multi-tenant SaaS product.
• Hands-on experience with AI/ML serving infrastructure: built and operated model inference pipelines under production load.
• Search architecture experience: designed and operated a search platform that handles diverse query types (keyword, semantic, or hybrid) across large structured or semi-structured datasets.
• Hands-on experience with data lake architectures at scale: Delta Lake or Apache Iceberg, schema evolution patterns, partition pruning, and the trade-offs between query performance and storage cost.
• Infrastructure fluency on AWS with Kubernetes or ECS. Can read a cost anomaly report, trace it to a root cause, and produce an action within the same week.
• Ability to write and defend architecture decisions: RFCs, trade-off documents, design reviews.
• Strong cross-team communication. Can write a document that produces alignment without a follow-up meeting to explain the document.
• Comfort operating across strategy, architecture, and operations in the same week: setting a multi-year architecture direction and reviewing a runbook gap are both in scope.

Nice to have

• Experience in regulated industries (biopharma, medtech, financial services) where compliance and data residency are first-class architecture constraints built in from the start.
• Familiarity with scientific data platforms, ELN/LIMS systems, or laboratory informatics ecosystems, including the structural constraints of instrument data.
• Experience designing and operating internal developer platforms as a product: roadmap, adoption metrics, deprecation strategy.
• Experience building partner integration programs at the architecture level: connector SDKs, reference implementations, integration certification criteria, and the developer experience that makes external parties self-sufficient.
• Exposure to lab instrument ecosystems (proprietary data formats, on-prem agent deployment, vendor certification workflows) or analogous hardware-adjacent integration work in medtech or industrial IoT.
• Prior experience as a founding or early platform architect at a Series B–D SaaS company scaling to enterprise.

Benefits

• Competitive compensation with equity
• Unlimited PTO
• Company-paid Life Insurance, LTD/STD
• 401(k)