What You’ll Work On

• 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.

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.

What We’re Looking For

• 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

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

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

Why Join

• 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

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