The Machine Learning team is expanding into large language models (LLMs) and needs to push the boundaries of generative AI by aligning models with human intent, optimizing training at scale, and deploying intelligent systems that operate in real-time, high-stakes environments.
Requirements
- Experience in ML research or engineering, with a focus on deep learning or generative models
- 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
Responsibilities
- 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
Other
- PhD in Computer Science, Machine Learning, or a related field—or equivalent practical experience
- A strong publication record in top-tier conferences such as NeurIPS, ICML, or ICLR
- Strong communication skills and a collaborative mindset
