Our Machine Learning team is expanding into large language models (LLMs), and we’re looking for bold, inventive minds to help us push the boundaries of generative AI.

As a Deep Learning Researcher, you will work on some of the most ambitious challenges in the LLM space: aligning models with human intent, optimizing training at scale, and deploying intelligent systems that operate in real-time, high-stakes environments. You will have access to extensive, high-quality proprietary datasets.  You’ll have the autonomy to explore novel ideas, the resources to scale them, and the opportunity to see your research power real-world trading systems.

What You’ll Do

• Lead and contribute to research initiatives that advance LLM capabilities, including alignment, fine-tuning, and efficient training
• Design and execute large-scale experiments, from data pre-processing to model evaluation and deployment
• Collaborate with world-class engineers, traders, and researchers to bring ideas from prototype to production
• Optimize model performance for structured tasks such as function calling, multilingual applications, and real-time inference

• PhD in Computer Science, Machine Learning, or a related field—or equivalent practical experience
• Experience in ML research or engineering, with a focus on deep learning or generative models
• A strong publication record in top-tier conferences such as NeurIPS, ICML, or ICLR
• Strong background in modern language modeling techniques such as LLM supervised fine-tuning, RLHF, reasoning models, embedding models, multimodal models, or agentic architectures
• Proficiency in Python and ML frameworks such as PyTorch (preferred), TensorFlow, or JAX
• Experience with large-scale distributed training, GPU optimization (CUDA/ROCm), or HPC environments
• Experience designing and operating large-scale data annotation and curation pipelines, including labeling tools, workflow orchestration, quality-control auditing, and learning feedback loops
• Demonstrated ability to take research from conception to production in high-stakes environments
• Strong communication skills and a collaborative mindset

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