Machine Learning Researcher | LLM | Reinforcement Learning | Foundational Models | Pre-Training | Hybrid, New York

ML Research Lead | LLM | Reinforcement Learning | Foundational Models | Pre-Training | Hybrid, New York

Location: New York (3–4 days in-office)
Stage: Series A | ~7-person team (scaling rapidly)

About the Company
We’re a frontier AI research lab building foundation models for financial markets.
Our mission is ambitious:

👉 Train the world’s best models for investing — and ultimately remove the need for manual trading altogether.

This is not incremental work. We are:
Training models end-to-end from scratch (not just fine-tuning)
Building reinforcement learning loops grounded in real P&L
Designing a domain-specific AI stack for financial decision-making

Backed by top-tier investors following our Series A, we are a small, high-calibre team scaling quickly.

The Role
We’re hiring a Foundation Model Training Lead to take ownership of our core models.
This is a deeply technical, high-impact role at the intersection of large-scale model training, reinforcement learning, and financial systems.

You will be responsible for the full lifecycle of model development, from pretraining through post-training optimization — shaping both the architecture and the training strategy.
For the right candidate, this role can evolve into a Head of AI / Research Lead position.

What You’ll Do
Lead the end-to-end training of large-scale foundation models
Design and implement pretraining and continued training strategies on financial data
Own model architecture decisions, including:
Mixture-of-Experts (MoE) design and routing
Tokenization strategies for financial data
Build and iterate on RL training loops tied to real-world trading performance (P&L)
Develop systems for training stability, scaling, and performance optimization
Define and execute data strategy (dataset construction, curation, filtering, labeling)
Work closely with engineering to build scalable training infrastructure
Contribute to the broader research direction and technical roadmap

What We’re Looking For
Proven experience training large-scale models end-to-end(not just fine-tuning existing models)
Strong background in deep learning and large model architectures
Experience with reinforcement learning in real-world or production settings
Hands-on work with MoE architectures and/or distributed training systems
Deep understanding of:
Training dynamics and instability
Scaling laws and optimization
Data quality and curation for large models
Ability to operate in a high-ownership, fast-moving environment

Nice to Have
Experience applying ML to financial markets or trading systems
Familiarity with low-latency or real-time systems
Prior experience in early-stage or research-heavy environments

Why This Role
Work on a greenfield problem at the frontier of AI + finance
Direct ownership over core model development
Opportunity to shape an entirely new category of AI systems
Clear path to Head of AI / Research leadership
Join at an early stage with outsized impact on company direction

How We Work
Small, highly technical team with deep focus and high velocity
Emphasis on first-principles thinking and experimentation
Tight feedback loops between research, models, and real-world outcomes
In-person collaboration in NYC (3–4 days/week)

ML Research Lead | LLM | Reinforcement Learning | Foundational Models | Pre-Training | Hybrid, New York
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