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.

The Role, In Plain English

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.

What You’ll Be Responsible For

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

What “Good” Looks Like in This Role

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.

Who You Are (Must-Haves)

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

Nice-to-Haves (Not Deal Breakers)

Experience with LLM reasoning systems or knowledge graphs

Exposure to Golang-based ML integrations

Prior experience supporting investor-facing products

How We Work

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

Why This Role Is Worth Your Time

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

Compensation & Practicalities

Base salary: $180,000 to $300,000 depending on experience

Equity included

In-office role based in New York

No agency candidates

Salary Range

$180,000 — $300,000 USD