ML Research Lead | LLM | Reinforcement Learning | Foundational Models | Pre-Training | Hybrid, [...]

Enigma; New York (NY); On-site; USD 180,000 - 240,000; Full time

Job summary

Enigma is hiring a Foundation Model Training Lead to own the core models, overseeing end-to-end development from pretraining through optimization in a deeply technical, high-impact role connected to financial systems.

Based in New York with 3–4 days in-office, you will lead end-to-end training of foundation models, design MoE and data strategies, and collaborate with a small, fast-moving team to push research toward production-ready systems.

Qualifications

• Proven experience training large-scale models end-to-end (not just fine-tuning).
• 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, scaling laws, and optimization; data quality and curation for large models.

Responsibilities

• Lead the end-to-end training of large-scale foundation models.
• Design and implement pretraining and continued training strategies on financial data.
• MoE design and routing implementation.
• Tokenization strategies for financial data.
• Build 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 broader research direction and technical roadmap.

Skills

Large-scale models

Deep learning

Reinforcement learning

MoE architectures

Distributed training

Data quality & curation

Job description

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
• 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
• 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)