Deep Learning Researcher
- Pay
- $250,000 – $300,000 per year
- Location
- New York City, NY · On-site
- Job type
- Full-time
- Level
- Senior
- Experience
- 3+ years
- Education
- Master's degree
- Listed
- 30 days ago
Skills & highlights for this role
- PyTorch
- transformers
- self-supervised learning
- distributed training
Also useful
- JAX
- TensorFlow
- reinforcement learning
- generative AI
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
Pay and benefits
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.
About IMC
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.
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.
From IMC's careers page, first listed on Rolev.ai on October 10, 2026. Rolev.ai is not the employer.
How Rolev.ai helps with this application
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Common questions
What is the salary for the Deep Learning Researcher role at IMC?
The Deep Learning Researcher posting at IMC states compensation of $250,000 – $300,000 per year. It is listed as a full-time position. Final pay may depend on location, experience, and interview evaluation on IMC's official career site.
Where is the Deep Learning Researcher job at IMC located?
This Deep Learning Researcher position at IMC is located in New York City, NY (On-site). Always check IMC's application page for specific state eligibility or relocation policies.
What qualifications are required for Deep Learning Researcher at IMC?
For the Deep Learning Researcher role, IMC highlights 3+ years of relevant experience; a master's degree; experience with PyTorch, transformers, self-supervised learning, distributed training. Rolev.ai can score your existing resume against these requirements and show any skill gaps before you apply.
How do I apply for the Deep Learning Researcher position at IMC?
This listing links directly to IMC's Greenhouse application page. You can apply directly on IMC's site, or use Rolev.ai to check your resume fit, generate a zero-hallucination tailored resume and cover letter for this posting, and autofill the Greenhouse application form in 1 click with the Rolev.ai Chrome extension. You always review the form and press Submit yourself.