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At Rogo, we are building Wall Street's first true AI analyst. Our mission is to empower finance professionals at the world’s top investment banks, private equity funds, and investment firms with AI that delivers unparalleled speed, accuracy, and insight. We are not just improving financial workflows; we are redefining them from the ground up. This is a unique opportunity to join a generational company at a key inflection point. With a rapidly growing client base, proven product-market fit, and backing from world-class investors, we are scaling quickly and defining a new category of enterprise AI. Our team is sharp, motivated, and deeply committed to the mission. We operate with intensity, take ownership of complex problems, and stay relentlessly focused on our users. If you thrive in a fast-paced environment, demand excellence, and want to help build the future of finance, we invite you to join us.
Job Responsibility:
Architect end to end learned systems combining the power of LLMs and Reinforcement Learning to automate entire financial workflows
Design and build large-scale infrastructure for evaluating answer and response quality, and directly feed this data back into learned systems as a reward
Partner closely with product managers and engineers to ensure every solution you build delivers genuine user value and is guided by real needs
Raise the bar for code quality, reliability and product velocity. Collaboratively, you'll push yourself and peers to develop technically and interpersonally.
Requirements:
Bachelors in Computer Science or related degree
3-6 years of experience working on large language model training or post-training (or equivalent PhD)
Highly proficient in writing LLM training code in PyTorch or JAX
Experience with a strongly typed language (e.g., Rust, C++, or Java)
Strong programming skills and general Computer Science knowledge
Nice to have:
Experience working directly in training or post-training LLM at a frontier lab (OpenAI, Anthropic, DeepMind, Meta)
Experience with deep reinforcement learning in any context (autonomous vehicles, robotics, or LLMs)
Experience working with data generated by human experts for model training