Deep Learning Researcher

IMC Trading; New York (NY); On-site; USD 250,000 - 300,000; Full time

Benefits offered by this job

Discretionary bonus

Paid leave

Insurance

Job summary

IMC Trading seeks a Deep Learning Researcher to push state-of-the-art techniques for real-world trading applications. You will join a team advancing deep learning research and infrastructure, including large-scale pre-training, predictive modeling, and representation learning.

You will lead projects on neural architectures, self-supervised learning, foundation models, and generative AI for financial markets, collaborating with traders and engineers to translate insights into scalable solutions.

Qualifications

• PhD or Master’s in a quantitative field.
• 3+ years of deep learning research and large-scale ML system development.
• Expertise in transformers, self-supervised learning, or generative models.
• Strong foundations in DL theory, optimization and training dynamics.
• Experience training and deploying large models in distributed environments.
• Strong Python skills and experience with DL frameworks (PyTorch, JAX, TF).
• Ability to communicate complex technical concepts clearly.
• Thrives in a collaborative, research-driven environment.

Responsibilities

• Build and improve deep learning models to enhance trading performance.
• Innovate in representation learning, transformers, foundation models, generative AI, RL, and other DL techniques.
• Research, prototype, and evaluate novel neural architectures for market state prediction and portfolio optimization.
• Support large-scale data curation, feature representation and multimodal learning.
• Collaborate with traders, researchers and engineers to translate insights into scalable solutions.
• Stay at the forefront of DL research and share findings with the team.

Skills

Deep learning research

Distributed computing

Python

PyTorch

JAX

TensorFlow

Communication

Collaboration

Research leadership

Education

PhD or Master’s in Computer Science / ML / AI / Mathematics / Statistics / Engineering

Tools

PyTorch

JAX

TensorFlow

Distributed systems

Job description

IMC is seeking a Deep Learning Researcher with a proven track record of developing and applying state-of-the-art deep learning techniques to solve complex real-world problems. This individual will join a team that is leading the advancement of deep learning research and infrastructure, including large-scale pre-training, predictive modeling and representation learning, to increasingly shape our trading.

The ideal candidate has deep expertise in modern AI and machine learning research and a passion for applying cutting‑edge techniques to new domains. Prior financial industry experience is not required. We are looking for someone who can bring fresh perspectives, rigorous scientific thinking, and strong technical leadership to help build a world‑class deep learning capability at IMC.

This is an opportunity to work at the intersection of AI research and quantitative trading, leading efforts spanning modern neural network architectures, self‑supervised learning, foundation models, generative AI, reinforcement learning, and custom deep learning approaches designed for financial markets.

Your Core Responsibilities

• Build and improve advanced deep learning models to enhance trading performance across multiple asset classes
• Innovate in areas such as representation learning, transformers, foundation models, generative AI, reinforcement learning, and other emerging deep learning techniques
• Research, prototype, and evaluate novel neural network architectures and training methodologies applicable to market state prediction, signal generation, execution optimization, and portfolio construction
• Support large-scale data curation, feature representation, self-supervised learning, and multimodal learning from structured and unstructured datasets
• Partner with quantitative traders, researchers, and engineers to translate market insights and business objectives into scalable deep learning solutions
• Stay at the forefront of academic and industry developments in deep learning and share relevant research with the team

Your Skills And Experience

• PhD or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, Statistics, Engineering, or a related quantitative field
• 3+ years of experience conducting deep learning research and developing large-scale machine learning systems
• Demonstrated expertise in modern deep learning architectures, including transformers, sequence models, representation learning, self-supervised learning, and/or generative models
• Strong understanding of the theoretical foundations underlying deep learning, optimization, statistical learning, and neural network training dynamics
• Experience training and deploying large-scale models in distributed computing environments; experience with large-scale pretraining is highly valued
• Strong programming skills in Python and hands‑on experience with modern deep learning frameworks such as PyTorch, JAX, TensorFlow, and associated ecosystem tools
• Experience building scalable ML infrastructure, experimentation platforms, and model deployment systems
• Strong communication skills with the ability to explain complex technical concepts to both technical and non-technical audiences
• Demonstrated ability to thrive in a highly collaborative, research-driven environment

The Base Salary range for the role is included below. Base salary is only one component of total compensation; all full-time, permanent positions are eligible for a discretionary bonus and benefits, including paid leave and insurance. Please visit Benefits - US | IMC Trading for more comprehensive information.

Salary Range

$250,000 - $300,000 USD

About Us

IMC is a research-driven trading firm where quantitative modeling, machine learning, and engineering shape how modern markets are traded. A stabilizing force in markets since 1989, we provide liquidity across trading venues, delivering the best outcome in value and risk management to investors. Using our own technology and capital, we build proprietary systems and algorithms that operate across global markets. Our researchers, traders, and engineers work as a collective, combining rapid experimentation, advanced infrastructure, and real‑time feedback to turn insight into execution and execution into advantage.