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Tessera Labs is building multi-agent AI systems that automate complex business workflows across enterprise platforms like SAP, Salesforce, Workday, Snowflake, and MuleSoft. The company is backed by Andreessen Horowitz and Foundation Capital, with a founding team drawn from Meta AI, Google Research, Microsoft AI, and major enterprise software vendors.
As a Full Stack Software Engineer, you will own meaningful product problems end to end on Tessera Mosaic, the company's AI-native platform where agents work alongside people to transform enterprise operations. You'll work across product, design, data, and infrastructure with significant ownership over both what gets built and how it's built.
Key responsibilities:
- Own ambiguous product and technical problems from clarification through shipping: define the problem, investigate underlying data and systems, propose approaches, align on tradeoffs, and deliver.
- Build data-intensive product features across the full stack: React frontends, APIs, data models, and backend services.
- Create AI-native workflows that make agent behavior legible and controllable—helping users understand what agents are doing, review their work, and intervene when needed.
- Integrate with enterprise systems of record (SAP, Salesforce, Workday), navigating complex data models, APIs, and real-world edge cases.
- Partner closely with product, design, and engineering to challenge assumptions, shape solutions, gather early feedback, and iteratively deliver business value.
- Help establish technical foundations through thoughtful architecture, reusable abstractions, shared components, engineering conventions, and technical discussions.
Requirements:
- Strong production experience building applications with React and TypeScript, plus backend experience designing APIs and services in Node.js, Python, Java, or comparable stack.
- Strong data fundamentals: comfort with relational data models, SQL, schemas, transformations, and performance/reliability considerations of data-intensive applications.
- Ability to own features from database to UI; you don't need equal depth everywhere but must be comfortable moving across the stack and learning as needed.
- Track record of turning ambiguity into action: define problems before solutions, ask good questions, make pragmatic tradeoffs, ship iteratively rather than waiting for perfect specs.
- Strong product judgment: understand and challenge the "why," contribute ideas beyond implementing requirements, balance speed with quality/security/maintainability.
- Collaborative, high-ownership approach: communicate technical decisions clearly, contribute constructively to team design and building, help other engineers succeed.
Nice to have:
- Experience building AI-native or agentic products (agent execution, tool use, observability, evaluations, human-in-the-loop workflows).
- Experience with ERP, CRM, HCM, or other complex systems of record, particularly SAP.
- Experience in fast-moving early-stage environments and modern AI-assisted development tools.
