Machine Learning Researcher

• Posted 17 hours ago • Updated 17 hours ago

Full Time

On-site

USD $250,000.00 - 300,000.00 per year

Fitment

Dice Job Match Score™

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Job Details

Skills

Deep Learning
Conflict Resolution
Problem Solving
FOCUS
Portfolio Optimization
Management
Data Acquisition
Unstructured Data
Mathematics
Statistics
Computer Science
Python
PyTorch
TensorFlow
JAX
Art
Machine Learning (ML)
Communication
Insurance
Backbone.js
Value Engineering
Finance
Quantitative Analysis
Business Operations
Research
Trading
Collaboration

Summary

{"description": "IMC Trading is seeking quantitative researchers with a proven track record to apply state-of-the-art machine learning & deep learning to solve challenging trading problems. This role is part of a central ML research team that collaborates across trading teams at IMC. The ideal candidate will have experience working with other researchers and engineers to build and continuously improve models, systems, and research tooling. We firmly believe that success for research-driven efforts lies in bringing together skills in ML, statistics, and trading intuition as well as a problem-solving mindset and pragmatism. This is an opportunity to dive deep into feature engineering and alpha research, and focus on applying a wide range of ML models as well as to perform research on building custom models.

Your Core Responsibilities:

Design and deploy machine learning models to enhance trading performance across various asset classes
Research, test and prototype new algorithmic ideas; deploy advanced ML techniques applicable to market prediction, signal generation, and portfolio optimization
Collaborate with quantitative traders, researchers, and developers to translate market insights into data-driven features and models
Manage data acquisition, preprocessing, and feature engineering for structured and unstructured data sources

Your Skills and Experience:

PhD or Master's in Engineering, Math, Statistics, Computer Science, or related quantitative field
2+ years of experience building applied ML models; previous experience in trading environment preferred
Proven expertise in developing and deploying predictive models
Strong programming skills in Python; proficiency in ML libraries such as PyTorch, TensorFlow, and/or high-performance libraries like Jax
Strong understanding of theoretical foundations of state-of-the-art ML models
Strong publication track record at ICML, ICLR, NeurIPS, or equivalent
Ability and desire to work in a collaborative team environment
Excellent written and verbal communication skills

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 global trading firm powered by a cutting-edge research environment and a world-class technology backbone. Since 1989, we've been a stabilizing force in financial markets, providing essential liquidity upon which market participants depend. Across our offices in the US, Europe, Asia Pacific, and India, our talented quant researchers, engineers, traders, and business operations professionals are united by our uniquely collaborative, high-performance culture, and our commitment to giving back. From entering dynamic new markets to embracing disruptive technologies, and from developing an innovative research environment to diversifying our trading strategies, we dare to continuously innovate and collaborate to succeed.", "salary_raw": "USD 250,000.00 - 300,000.00 per year"}

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 90922487
Position Id: 24058414
Posted 17 hours ago

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