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