Deep Learning Researcher
$250k - $300k • New York, United States • Full-time
Posted 11mo ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
Job Location
New York, United States
Also open in Hong Kong, Hong Kong; Sydney, Australia
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Research Engineer
About the job
IMC is seeking a Deep Learning Researcher to join a team advancing deep learning research and infrastructure, focusing on large-scale pre-training, predictive modeling, and representation learning to shape trading strategies. The ideal candidate will possess deep expertise in modern AI and machine learning research, applying cutting-edge techniques to new domains. This role offers the opportunity to work at the intersection of AI research and quantitative trading, leading efforts in neural network architectures, self-supervised learning, foundation models, generative AI, and reinforcement learning, with a focus on financial markets.
Responsibilities
- Build and improve advanced deep learning models to enhance trading performance.
- Innovate in areas like representation learning, transformers, foundation models, generative AI, and reinforcement learning.
- Research, prototype, and evaluate novel neural network architectures and training methodologies for market prediction, signal generation, execution optimization, and portfolio construction.
- Support large-scale data curation, feature representation, self-supervised learning, and multimodal learning.
- Translate market insights and business objectives into scalable deep learning solutions in partnership with traders, researchers, and engineers.
- Stay current with deep learning research and share findings with the team.
Requirements
- PhD or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, Statistics, Engineering, or a related quantitative field.
- 3+ years of experience in deep learning research and developing large-scale machine learning systems.
- Demonstrated expertise in modern deep learning architectures (transformers, sequence models, representation learning, self-supervised learning, generative models).
- Strong understanding of deep learning theory, optimization, statistical learning, and neural network training dynamics.
- Experience training and deploying large-scale models in distributed computing environments; large-scale pretraining experience is highly valued.
- Strong programming skills in Python and experience with deep learning frameworks (PyTorch, JAX, TensorFlow).
- Experience building scalable ML infrastructure, experimentation platforms, and model deployment systems.
- Strong communication skills to explain complex technical concepts.
- Ability to thrive in a collaborative, research-driven environment.
Benefits
- Discretionary bonus
- Paid leave
- Insurance