Staff AI Engineer

Building the next generation of wealth

LocationSan Francisco, CA, US

TypeFull-time

* Architecting LLM-powered, agentic systems for private market research, analysis, and decision support * Designing hybrid reasoning pipelines that combine LLMs with retrieval, structured financial data, rules, and tools * Building robust RAG pipelines over unstructured, noisy, and proprietary data (PPMs, decks, filings, internal memos) * Developing evaluation frameworks for reasoning quality, faithfulness, latency, and cost * Implementing observability, debugging, and failure-handling for multi-step AI workflows * Partnering closely with business and design to translate ambiguous user needs into reliable intelligent behavior * Raising the bar on AI engineering practices across the team through example and mentorship

**Concrete Example **Build an AI deep research analyst that can synthesize deal documents, market data, news articles, and comparable deals into actionable, well-sourced insights—while surfacing nuances rather than hiding it.

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Why This Is Hard

* Product intuition matters: We’re building a sticky, high-value product for real investors—not a demo or internal research tool. * High-stakes domain: Private market investing requires accuracy, explainability, and calibrated uncertainty. “Mostly right” is not acceptable. * Data complexity: There is no clean source of truth. Data is fragmented, sparse, and often contradictory. * Reasoning over generation: The challenge is building systems that reason, compare tradeoffs, and surface uncertainty—not just generate fluent text. * Agent reliability: Multi-step, tool-using agents must behave consistently in production, not just in demos. * Evaluation is unsolved: You’ll help define what “good” looks like when traditional ML metrics fall short. * Trust as a system property: Explainability, sourcing, and failure modes are core technical requirements—not UX afterthoughts.

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VisaUS citizen/visa only

Apply to this role

* 6+ years of software engineering experience, with deep hands-on work in applied AI / ML systems * Strong fundamentals in Python and backend system design * Proven experience with LLMs (prompting, fine-tuning, RAG, agentic workflows, or evaluation tooling) * Experience owning ambiguous, high-impact systems from concept to production * Comfort making architectural tradeoffs under real-world constraints * Ability to think at the system level while still shipping high-quality code * High product intuition and a strong sense of responsibility for user outcomes

Bonus

* Experience in fintech, data-intensive products, or regulated environments

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How We Work

* Small, lean team with high ownership and minimal bureaucracy * Direct access to users and fast feedback loops * Strong bias toward clarity, correctness, and speed * High standards for technical rigor where trust matters

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What Success Looks Like

* AI systems that customers trust and rely on—not just experiment with * Measurable improvements in reasoning quality, reliability, and latency * Clear architectural patterns that scale with product complexity * A higher technical bar across the team through example and mentorship

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Equity0.50% - 2.00%

* Work on real, unsolved AI engineering problems in production * Develop systems used for real financial decision-making * Build the technical foundation of an AI-native platform at an early but high growth stage * Competitive compensation and meaningful equity

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If you’re excited about building serious AI systems—and want real ownership—we’d love to talk.

Apply to this role