TetraScience

Architect

• Architect the Exchange Layer: Design and own the Common Data Models (CDMs) that serve as the universal language for scientific data across our customer base. Move the platform from bespoke, one-off mappings to a standardized "exchange layer" that ensures interoperability.
• Empower Forward Deployed Engineering: Create the data contracts and standardized definitions that FDEs rely on. Your models will be the toolkit that allows them to deploy faster and with higher confidence, knowing they are building on a stable, consistent semantic foundation.
• Standardization vs. Flexibility: Strike the strategic balance between rigid global standards (for cross-customer exchange) and local flexibility. Define the core "immutable" aspects of the model versus where extension is permitted.
• The "Forest" – Business Alignment: Translate high-level business goals (e.g., "accelerate time-to-insight for biologics") into concrete data modeling strategies. Ensure our semantic roadmap directly supports the scientific questions our customers—and our internal teams—need to answer.
• The "Trees" – Hands-on Modeling: Roll up your sleeves to design and implement complex ontologies and taxonomies. Model intricate scientific relationships (e.g., linking a "Cell Line" in an ELN to "Flow Cytometry Results") with precision.
• Software & Data Engineering Integration: Work directly with Engineering to architect the software systems that consume these models. Ensure that the "perfect" ontology does not break query performance or system scalability.
• Data Contracts & Governance: Establish the "rules of the road" for data quality and consistency. Define how data contracts are versioned, enforced, and evolved, ensuring that downstream consumers (AI teams, FDEs, Scientists) can trust the data structure.
• Scientific Translation: Partner with Scientific Business Analysts to decode the complexity of biopharma R&D. Turn ambiguous scientific requirements into rigorous, machine-readable data structures.
• Interoperability: Architect models that ensure our data is FAIR (Findable, Accessible, Interoperable, Reusable) and ready for downstream AI/ML applications.

Data & Semantic Model Architect

Engineer

• Apply Lessons Learned From Domain Leaders: Bring practical, everyday lessons learned and experiences from other areas, domains and professions to this problem space.
• Engineer Semantic Services: Co-design and build the Service-Oriented Architecture (SOA) components responsible for the lifecycle of semantic data. Your software enables ingestion, abstraction, curation, and publication, versioning, governance & distribution of semantic resources—encompassing formal biomedical ontologies, standard terminologies, and reference lists—integrating them directly into data-in-flight services.
• Data Engineering for Continuous Modeling: Develop and deploy ETL pipelines that lift instance-level data into formal vocabularies. You will ensure these pipelines capture the nuanced aspects of the model, including complex relationships, properties, and constraints.
• Living Ontology Development: Build services that project formal semantic meaning across the Tetra ecosystem, helping to formalize a "living," real-world ontology that evolves with our data and its usage.
• Partner as a Force Multiplier: Collaborate with Scientific Data Engineers, Architects, and Business Leads to integrate semantic artifacts technically. You will provide subject matter expertise, coaching, and training on the governance of controlled vocabularies, ensuring formal semantics and structure are applied consistently across platform applications.
• Vocabulary Management: Develop software enabling management of the full lifecycle of our formal vocabularies. You will implement robust systems for versioning, deprecating, and migrating vocabularies across our customer base to ensure seamless operations.
• Engineering Mentorship: Serve as the "Informatics SME" for the engineering organization. You will partner with and mentor technical team members—ranging from pure software engineers to scientists—on how to leverage semantic artifacts to provide data with formal definitions, meaning, and context.
• Data Exchange Standards: Co-design the "handshake" protocols for data exchange between platform components. You will iteratively develop these standards to ensure that data leaving one system is deterministically understood by another, handling the complex mapping and transformation logic required for true syntactic and semantic interoperability.

Senior Informatics Engineer

Data Engineer

• You will be a critical team member in a unique partnership to industrialize Scientific AI.
• Engage directly with customers onsite a couple of days per week in the St Louis area, building strong relationships, deeply understanding their scientific data challenges and requirements, and accelerating solutions.
• Design and implement extensible, reusable data models that efficiently capture and organize scientific data for scientific use cases, ensuring scalability and future adaptability.
• Translate scientific data workflows into robust solutions leveraging the Tetra Data Platform.
• Own, scope, prototype, and implement solutions including:
• - Data model design (tabular & JSON)
• - Python-based parser development.
• - Lab software (e.g., ELN/LIMS) integration via APIs.
• - Data visualization and app development in Python (using app frameworks like Streamlit and plotting tools like holoviews and Plotly).
• Collaborate with Scientific Business Analysts (SBAs), customer scientists and applied AI engineers to develop and deploy models (ML, AI, mechanistic, statistical, hybrid).
• Programmatically interrogating proprietary instrument output files.
• Dynamically iterate with scientific end users and technical stakeholders to rapidly drive solution development and adoption through regular demos and meetings.
• Proactively communicate implementation progress and deliver demos to customer stakeholders.
• Collaborate with the product team to build and prioritize our roadmap by understanding customers’ pain points within and outside Tetra Data Platform.
• Rapidly learn new technologies (e.g., new AWS services or scientific analysis applications) to develop and troubleshoot use cases.
• Must be able to travel to client sites in St.Louis, Indianapolis, Chicago regions.

Scientific Data Architect

Product Manager

• Dual Service Strategy (Inference & Training): Define the roadmap for two core service pillars:
• Training Services: Orchestrating elastic, cost-optimized compute (GPU/CPU) for model training and experiment tracking.
• Inference Services: Managing the deployment of models into high-availability, low-latency API endpoints.
• Ease of Development & Deployment: Radicalize the user experience for ML Engineers. You will build self-service "push-button" deployment workflows that abstract away the complexity of Kubernetes and cloud networking.
• Lineage & Reproducibility: Ensure every model has a clear "paper trail." You will define how we capture the lineage between data versions, training code, and production artifacts—a critical requirement for Biopharma compliance.
• Observability & Governance: Build the tools to monitor model health in production. This includes infrastructure-level metrics (latency/memory) and model-level observability (drift/performance) to ensure system reliability.
• Technical Stakeholder Engagement: Partner with Scientific IT and Platform Engineering to ensure our services integrate seamlessly with existing enterprise identity (IAM) and security frameworks.
• Backlog & Execution: Act as the "CEO of the Service," translating complex infrastructure needs into clear, actionable epics and user stories for a high-performing engineering team.

Senior Product Manager – Model Hosting Infrastructure

• - Manage a team of SDEs. Supervise their career growth and performance.
• - Work with cross-functional teams to understand business requirements, gather insight into potential positive outcomes, recommend potential outcomes, and build a solution based on consensus.
• - Take ownership of building data models, prototypes, and solutions that drive customer success.
• - Use LLM to build comprehensive data schemas and parsers for pre-clinical data (main data sources: R&D lab instruments, manufacturing, CRO, CDMO, ELN, LIMS) with various data formats: .xlsx, .pdf, .txt, .raw, .fid, many other vendor binaries
• - Extract reusable schema components and parsing functions, and productize them into Python libraries
• - Build high-quality data pipelines with full unit test and integration test coverage to produce high-fidelity data
• - Build data applications, reports, and dashboards using React, Streamlit, Jupyter notebook, etc.
• - Drive value for the customers - verify the solution fulfills their requirements and provides value
• - Quality gatekeeper: design with quality backed by unit tests, integration tests, and utility functions.
• - Promote team-wide process/technology improvements on product quality and developer experience
• - Rally the team to finish Agile Sprint commitments. Actively surfacing team inefficiencies and striving to resolve them.
• - Driven by results. Have the pragmatic urgency to resolve blockers, unclear requirements, and make things happen.

Tech Lead/Manager, Scientific Data Engineering

Solutions Engineer

• Providing a strategic technical partnership with Customers, you will bridge the gap between the business objectives and technical execution.
• You will be the primary technical leader for scientific end-users to design, architect, and deliver highly impactful solutions to pharmaceutical and biotech customers.
• You will work to understand scientists' and R&D IT analysts' needs, analyze their data environment, assist in the design and implementation of our solutions and enable customers to achieve faster ROI, reduced risk, smoother scaling, accelerated product adoption and ensure the long-term success of projects.
• Primary technical point of contact across strategic accounts
• Lifecycle engagement & adoption acceleration from presales to delivery
• Technical health & risk management
• Strategic planning and stakeholder management across sales, product management and engineering teams
• Sales enablement & growth support
• Deep and wide technical and science senior relationships
• Deeply understand Customer scientific data and AI architecture & goals
• Provide proactive, prescriptive technical guidance
• Act as a trusted advisor aligned to Customer outcomes
• Drive adoption & success plan execution
• Conduct regular technical reviews
• Identify risks & recommend optimizations
• Lead technical strategy for expansions
• Support POCs & co-innovation
• Manage resolution for escalated issues
• Accelerate time to value
• Improve stability & uptime
• Strategic roadmap alignment
• Prioritized support & faster resolutions
• Higher ROI through continuous optimization
• Support growth through hands on sessions and enablement presentations
• Showcase tailored demonstrations to prospects, to both small and large audiences
• Solution Mapping: Map business requirements to technical solutions.
• ROI Calculation: Determine potential return on investment for proposed solutions.
• Relationship Building: Cultivate relationships with clients, sales teams, and technical teams.
• Working with the science and technology Partners such as Databricks and Google
• Negotiation Skills: Collaborate with clients and sales teams to reach beneficial agreements.
• Product Knowledge: Develop a deep understanding of the company's product or service offerings.
• Demo Development: Design and showcase tailored demonstrations to prospects.
• Proposal Writing: Contribute to RFPs/RFIs with detailed technical specifications and solutions
• Timeline Management: Ensure projects are kept on track and within scope.
• Stakeholder Management: Coordinate with various stakeholders to ensure the smooth flow of presales activities.

Senior PreSales Solutions Architect

DevOps Engineer

• Collaborate with product and engineering teams to drive and enhance the entire lifecycle of our products, from design and development to deployment and operation.
• Work closely with clients to deploy and troubleshoot our products in clients' AWS environments ensuring smooth integration and optimal performance.
• Develop CloudFormation templates, Terraform modules, Python scripts, deployment frameworks, monitors, and self-healing tools to automate processes and improve efficiency.
• Assist the software engineering team in building accurate monitoring and metrics systems for applications before they go into production.
• Manage the internal AWS environments and network, ensuring stability, security, and scalability while keeping costs in check
• Participate in meetings with potential clients, working alongside solution architects to address their questions and concerns regarding integration of our products into their network and AWS accounts.
• Maintain up-to-date documentation on deployments, processes, and standard operating procedures.

Platform Engineer

• Lead by example to architect and code the next-generation scientific search engine, building a system that can reason over billions of scientific data points—from chemical structures (SMILES) to unstructured lab documents and instrument data.
• Engineer sophisticated hybrid search pipelines that blend sparse (keyword), structured (metadata), and dense (vector) retrieval. You will go beyond out-of-the-box OpenSearch to design custom ranking logic, reciprocal rank fusion, and relevance tuning that surfaces the exact "needle in the haystack" for drug discovery.
• Own and operate the Search Platform infrastructure, ensuring high availability, scalability, performance, and observability across indexing, embedding generation, and query execution.
• Develop and maintain backend services and APIs in Python and TypeScript that power search capabilities for scientists, data engineers, and AI applications.
• Collaborate with Applied AI Scientists to integrate embeddings, transformer models, and chemical fingerprints into production search workflows.
• Architect and implement scientific entity resolution and knowledge graph pipelines to transform raw text into interconnected knowledge. You will design systems that extract and link chemical and biological entities (NER/NED) from unstructured documents, enabling the search engine to "understand" relationships between compounds, targets, and assays.
• Continuously improve search quality through evaluation metrics such as precision@K, recall@K, MRR, and relevance testing with real scientific use cases.
• Ensure security, compliance, and tenant isolation as part of operating search services in enterprise bio-pharma environments.
• Contribute to architectural decisions, technical strategy, and platform-wide improvements to accelerate scientific insight generation.

Lead Platform Engineer – Search Platform

• You will be leading the Scientific Data Engineering (SDE) Team and helping build Tetra Data and productizable solutions, which is the foundation of the Data Engineering layer. We are looking for a data engineer who is experienced, hands-on, and can also provide mentorship to junior team members. As a Senior Scientific Data Engineer, you should be comfortable leading internal design sessions and architecting solutions. You will work directly with Product Managers and Solution Architects to gather business and data design objectives, resulting in production-based solutions. As a Senior Scientific Data Engineer, you will be a team-focused leader, have excellent data engineering skills, supervise and collaborate on project executions, and have a high commitment to customer success by delivering mission-critical implementations.
• Our success is defined by collaboration. You will have tremendous support to achieve your objectives, from a variety of teams, both internal and external.
• - Work with Product Managers and Solution Architects to understand business requirements, gather insight into potential positive outcomes, recommend potential outcomes, and build a solution based on consensus.
• - Take ownership of building data models, prototypes, and integration solutions that drive customer success.
• - Research and prototype data integration strategy for scientific lab instrumentation, prototype file parsers for instrument output files (.xlsx, .pdf, .txt, .raw, .fid, and many other vendor binaries).
• - Quality gatekeeper: design with quality backed by unit tests, integration tests, and utility functions.
• - Lead team-wide process/technology improvements on product quality and developer experience
• - Rally the team to finish Agile Sprint commitments. Actively surfacing team inefficiencies and striving to resolve them.
• - Driven by results. Have the pragmatic urgency to resolve blockers, unclear requirements, and make things happen.
• - Provide mentorship to junior SDEs and show leadership in every front