Research & Model Intelligence Lead

The Opportunity

As Research & Model Intelligence Lead, you'll take full ownership of the systems and science that power our edge — from model research and fine-tuning to inference optimization, evaluation frameworks, and the AI agent intelligence layer.

You will be expected to lead a team that builds, evaluates, and deploys models that support our investment activities.

This is a senior role with real leadership weight. You'll lead a team of ML engineers, NLP specialists, and quantitative researchers — setting research direction, running evaluation cycles end-to-end, and ensuring that model improvements translate to trading performance. You'll partner tightly with traders, strategists, and platform engineers.

We're looking for an AI-native, agentic engineer-researcher: someone who instinctively uses LLMs and modern tooling to accelerate experimentation, evaluation, and iteration — and teaches their team to do the same. You create leverage through models, people, and rigorous evaluation.

What You'll Do

• Own the research and model intelligence domain
• Take end-to-end ownership of the systems that generate, evaluate, and serve our trading intelligence — including fine-tuned LLMs, prompt engineering pipelines, NLP signal extraction, agent architectures, and evaluation infrastructure.
• Lead and scale a team
• Act as a true people leader: set research direction, coach performance, run planning, and create clarity across a team spanning ML engineering, NLP, quantitative research, and agent development.
• Bridge research and production
• Turn model improvements into deployed trading performance. Own the full pipeline from hypothesis through evaluation to production serving — keeping it fast, reliable, and measurable.
• Partner with senior stakeholders
• Work directly with trading strategists, execution engineers, and platform leads. Translate trading needs into research priorities and keep everyone aligned as strategies evolve.
• Build with an AI-first mindset
• Use LLMs and agentic workflows to accelerate research processes — including experiment design, evaluation automation, literature synthesis, and knowledge management.
• Architect for inference quality and speed
• Design model serving, evaluation, and agent orchestration systems that are observable, reproducible, and optimized for performance. Own tradeoffs between model capability and serving performance.
• Set the standard
• Define how the team designs experiments, evaluates results, reviews model performance, and ships to production — both scientifically and operationally.

About You

• Strong people leader with experience running technical teams through real delivery
• Systems thinker who understands the full lifecycle from research to production inference
• Comfortable operating in ambiguity and bringing structure through experimentation
• Strong project management instincts — able to sequence research bets and manage evaluation cycles
• AI-native and scrappy — focused on leverage, automation, and modern tooling
• Values rigor, measurable outcomes, and impact over complexity or consensus

Experience That Helps (But Isn’t Dogma)

• Experience leading ML, NLP, or research teams shipping models in high-stakes or latency-sensitive environments
• Ownership of evaluation and continuous monitoring systems for deployed models
• Experience with LLM fine-tuning, prompt optimization, inference serving (quantization, multi-provider racing, GPU orchestration), or agent architectures
• Strong applied ML or quantitative research background with a focus on production deployment
• Curiosity, learning speed, and ownership mindset (financial markets experience not required)

Nice to Have

• Experience managing senior cross-functional stakeholders and driving alignment
• Background in trading, finance, crypto, or similar domains with direct P&L impact
• Familiarity with NLP for news or event-driven signal extraction
• Ideally based within ±3 hours of EST

Compensation & Package

Base Salary + Benefits Package + Performance related bonus (TBD on %)