AI Agent Engineer
Tessera Labs - San Jose Office (HQ), San Jose, California, United States, San Francisco Office
Posted Sep 5, 2025
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
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- Assessment
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- Deadline
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Where they hire
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About this role
AI Agent Engineer San Jose Office (HQ), San Jose, California, United States, San Francisco Office About Tessera Labs Tessera Labs is redefining how enterprises adopt and operationalize Artificial Intelligence. Backed by Foundation Capital and led by a world-class founding team, we build multi-agent AI systems that can automate complex business workflows across platforms like SAP, Salesforce, Workday, Snowflake, MuleSoft, and more. Our mission: Bring real AI automation to the enterprise - with speed, precision, and measurable impact. We move fast, operate with extreme ownership, and build at the frontier of applied AI. Job Summary As we build to the next level, we're looking for a top-quality AI engineer with a strong focus on AI agents - someone who knows how to leverage LLMs, both open-source and closed, in combination with complex tool-calling hierarchies and operational patterns. This is an opportunity to build scalable, secure, and high-impact AI technologies for the future of enterprise. If you thrive in high-growth, fast-moving environments and are passionate about AI, ML, and AI agents, we want to hear from you. Key Responsibilities 1. AI Agent Building (60%) - Develop complex multi-step agentic pipelines around open- and closed-source LLMs. - Build tools enabling AIs to interact with critical enterprise software platforms. - Ensure AI designs align with business objectives and industry best practices. 2. ML development and fine-tuning (30%) - Create data generation and processing pipelines to train and fine-tune custom ML models. - Implement and manage training and evaluation pipelines for LLMs. 3. Deployment and
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