Machine Learning and AI Engineer - New York

You have built real AI systems that ship, break, and get fixed under pressure.
You care less about model demos and more about decisions that move capital.
You want your work used daily by professional investors, not buried in notebooks.
You are comfortable owning hard problems end to end.
If you want a calm job optimizing benchmarks, this is not it.

You will build and evolve the AI systems that power Reflexivity’s investment insights.
This role exists because off-the-shelf models and generic pipelines are not enough.
You will work at the intersection of reasoning engines, proprietary data, and real market impact.
Your work directly affects how investors understand earnings, risk, and market catalysts.

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

After 3 months:
You understand the product, data flows, and investor use cases deeply. You ship meaningful improvements.

After 6 months:
You own major parts of the AI stack. Your work improves insight quality and latency measurably.

After 12 months:
You are a technical reference point for AI decisions. You raise the bar for how AI is built at Reflexivity.

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

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

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

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