Machine Learning Researcher - LLM

Overview

On Site
Full Time

Skills

Large Language Models (LLMs)
Generative Artificial Intelligence (AI)
Trading
Evaluation
Collaboration
Multilingual
Real-time
Computer Science
FOCUS
Deep Learning
Language Models
Management
Reasoning
Python
Machine Learning (ML)
PyTorch
TensorFlow
JAX
Training
GPU
Optimization
CUDA
HPC
Workflow
Orchestration
Quality Control
Auditing
Research
Communication
Recruiting

Job Details

Overview

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

What we're looking for
  • 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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