Join a small, fast-moving team where you’ll explore, invent, and discover.We’re training foundational models for financial applications on unique, multimodal datasets.What you’ll doFormulate and pursue original research agendas (reasoning, tool connectivity, robustness, alignment, data/compute efficiency).Follow Ideas to PoC to MVP to training to validation, owning all steps of the process with deep collaboration with Research Engineers (see other open role).Build rigorous evaluation suites through simulated environments and real time evaluation.Prototype fast with real data; partner with Research Engineers to take promising ideas to production.You might be a fit if youAre a self-starter who thrives on coding autonomy and project collaboration within small-team, high velocity environments.Have strong ML research chops (theory and practice), and write clean, reproducible code (e.g. PyTorch/JAX).Think in systems: data quality, scaling laws, evaluation, and deployment constraints - not just model internals.Prefer clear principles, low ego and prefer collaboration over titles and politics.Helpful backgrounds (any of the following)Frontier labs experience (e.g., Anthropic, OpenAI, DeepMind); orNovel discovery through published work (NeurIPS/ICML/ICLR); orLed research in an applied setting (startups, open-source, or products at scale); orStandout independent achievement (notable OSS, benchmarks, or widely used methods).Research areas we’re excited aboutEfficient training (HRM, distillation, sparse/MoE, FSDP, etc.).Reasoning and tool use (automated trading and agentic interaction with data sources).Multimodal Inputs and Outputs (multiple forms of text and time-series both in and out).What we offerA well-funded trading firm expanding into AI research - your ideas set direction and standards.Real ownership from hypothesis to deployment.Competitive base with meaningful upside tied to research impact.A culture optimized for deep work, fast learning, and doing the right thing.If this sounds like you, apply.
3 months ago