ML Research Lead | 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