AI/ ML Engineer

Job Description

You have built real AI systems that ship, break, and get fixed under pressure.

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You care less about model demos and more about decisions that move capital.

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You want your work used daily by professional investors, not buried in notebooks.

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You are comfortable owning hard problems end to end.

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If you want a calm job optimizing benchmarks, this is not it.

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The Role, In Plain English

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You will build and evolve the AI systems that power our investment insights.

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This role exists because off-the-shelf models and generic pipelines are not enough.

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You will work at the intersection of reasoning engines, proprietary data, and real market impact.

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Your work directly affects how investors understand earnings, risk, and market catalysts.

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What You’ll Be Responsible For

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• Design, fine-tune, and deploy ML and LLM-driven systems used in production by professional investors
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• Build and maintain inference pipelines that are fast, observable, and reliable
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• Integrate OpenAI, Gemini, and Anthropic models into reasoning and knowledge systems
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• Work closely with backend engineers to productionize models in Golang-based services
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• Improve signal quality, not just model accuracy
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• Review code and designs with a bias toward long-term maintainability
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What “Good” Looks Like in This Role

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After 3 months:

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You understand the product, data flows, and investor use cases deeply. You ship meaningful improvements.

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After 6 months:

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You own major parts of the AI stack. Your work improves insight quality and latency measurably.

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After 12 months:

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You are a technical reference point for AI decisions. You raise the bar for how AI is built here.

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Who You Are (Must-Haves)

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• 1 plus years building ML or applied AI systems in production
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• Startup experience working on a core product, not a side project
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• Strong Python skills and experience integrating with backend systems
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• Hands-on experience with AI-assisted coding tools like Cursor or Claude Code
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• Fintech experience or strong personal investment background
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• Comfortable owning outcomes, not just tasks
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Nice-to-Haves (Not Deal Breakers)

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• Experience with LLM reasoning systems or knowledge graphs
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• Exposure to Golang-based ML integrations
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• Prior experience supporting investor-facing products
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How We Work

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• In-office team with high trust and high ownership
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• Direct communication, minimal process, strong opinions backed by data
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• Engineers are expected to think about product impact, not just code
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• We move fast when it matters and slow down when correctness matters more
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Why This Role Is Worth Your Time

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• Direct influence on how professional investors make decisions
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• Hard problems at the edge of AI, data, and finance
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• Real ownership and technical autonomy
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• Senior peers who care about quality and outcomes
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Compensation & Practicalities

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• Base salary: $180,000 depending on experience
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• Equity included
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• In-office role based in New York
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• No agency candidates
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