Applied AI Engineer
Rowspace - San Francisco, California, United States, New York City
Posted Feb 4, 2026
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
- Cover letter
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Applied AI Engineer San Francisco, California, United States, New York City The Role As an Applied AI Engineer, you'll bring the frontier of AI research and engineering to Rowspace, transforming how our platform understands and surfaces critical financial insights. In a typical week, you might build and productionize an agent in response to customer feedback, design evaluation frameworks for AI performance, and implement advanced retrieval systems to make the most of AI for finance workflows. What You'll Do - Implement and adapt cutting-edge AI research to build systems that understand financial context and business concepts - Design retrieval pipelines that extract and synthesize insights from unstructured sources (PDFs, emails, presentations) with intelligent conflict resolution - Build agentic workflows that enable AI systems to reason about financial relationships, perform benchmarking, and conduct comparative analysis - Construct and maintain knowledge graphs that capture complex financial relationships, entity connections, and market dynamics - Create evaluation frameworks to measure and improve AI performance across investment research, due diligence, and portfolio monitoring tasks - Design and implement systems that maintain traceability from AI outputs to primary sources - Experiment with the latest LLM orchestration patterns and context management techniques for financial use cases - Shape technical direction and best practices for AI application development at Rowspace - Iterate rapidly with direct customer input, continuously refining AI systems based on real-world usage and feedback - Deploy and operate production systems end-to-end, with strong ownership over reliability, performance, and maintainability across the stack What You Have -
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