Role description
TetraScienceThe role
TetraScience is building TetraOS - the category defining Scientific OS for biopharma. The Tetra Platform is the foundation for developing, delivering and operating our enterprise grade, secure, compliant Scientific Data and AI capabilities customers build upon. After achieving strong product-market fit and traction, TetraScience is entering a growth scaling phase where we are expanding our industry partnerships and developer experience to rapidly build the foundations of AI-native scientific data and workflows in production.
In this role, you will own the Tetra Platform architecture, its evolution, and the partner integrations that extend it. This is an IC leadership role. You will define the platform architecture aligned with the product and business strategy. You will inform roadmap alignment decisions and trade-offs.
The scope of this role is intentionally broad. We are looking for experienced candidates who have gone deep in two to three technology domains to gain breadth and depth of architectural, execution and operational experience. Your experience spans startups and large companies, so you understand the trade-offs at different stages and scales. Strong candidates will have deep, diverse experience with operational excellence and agility in building in a dynamic business context.
The Tetra Platform spans the following diverse technology domains:
- Enterprise Data & Agentic AI Platform: Multi-tenant cloud architecture, Durable workflows, AWS, Databricks, RBAC/ABAC, IAM, observability and infrastructure chargebacks.
- Data, Knowledge, and Developer Products: Search, Semantic layer foundations, knowledge and ontology layers and products, external developer platforms.
What you’ll own:
- Enterprise Platform: Tenancy, IAM, Compliance and control plane that enterprise customers use to govern their scientific data environment: SSO/SAML/OIDC, fine-grained RBAC / ABAC for SQL access.
- 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 lifecycle, inference and training on platform, Agentic IAM and DX, telemetry, observability and frameworks that keep scientific AI outputs traceable and operable under production load.
- Developer Platform: External builder platform, low and high code scientific solution development experiences, golden path tooling for partner integrations, SDKs and adoption metrics.
- Developer Productivity: Developer throughput as a first-class metric: toolchain ownership, local/prod environment parity, and friction reduction from commit to deployment.
- Acceleration and adoption of Tetra’s Scientific Use Case Library: Data Products and Workflows Platform, IDS design standards and evolution, and the data access layer that AI workloads and downstream pipelines depend on.
- Partner Integrations: Integration architecture for lab instruments 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
- Tetra’s Authn/Authz architecture is streamlined and well integrated across data, compute and Agentic AI platform layers.
- AI/ML infrastructure has a clear architecture and roadmap for MLE inference and training use cases, with strong operational telemetry, cost visibility and governance.
- The Developer Platform has streamlined experiences, SDKs and standard templates for scientific use cases to start from, with adoption and delivery by multiple scientific use case teams. A reusability architecture that enables compounding contributions from the ecosystem including Tetra, partner and customer builders.
- Operational excellence with a future-proof O11y architecture rolled out, with production services and AI / ML workloads having metrics and SLOs defined, monitored and managed.
- Cost governance with customer chargeback attribution architecture and operationalized with the finance and field teams.
- Data Products and Workflow Primitives platform architecture built out 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 instruments 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.
Demonstrated architectural and execution leadership roles in multi-tenant cloud architecture and data intensive enterprise products at scale. You have been the person who got paged to production incidents, not just the person who designed it. You will bring earned expertise in at least two of the following:
Identity and authorization architecture at enterprise scale, SSO federation, fine-grained authz over data access.
Experience with AI/ML serving infrastructure: you have built and operated model inference pipelines under production load.
Search architecture experience: you have 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, warehouse or lakehouse 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. You can read a cost anomaly report, trace it to a root cause, and produce an action within the same week.
Ability to write, review and defend architecture decisions: RFCs, ADRs, technical trade-offs and design reviews.
Strong cross-team communication. You 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, cost-to-serve 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 Medical, Dental, Vision Insurance
- LTD/STD
- 401(k)
- The salary range for this position is $200K-$270K USD. The salary range posted reflects our target baseline for this role. Final compensation is determined by a thorough evaluation of factors including the candidate’s specific experience, localized market data, and internal team equity.