Senior ML Engineer, Applied Machine Learning
Red Cell PartnersSalary not specified
- Full Time
- Senior (5 to 8 years)
Not SpecifiedCompensation
Junior (1 to 2 years), Entry Level & New GradExperience Level
Full TimeJob Type
UnknownVisa
AI & Machine Learning, Research, Software EngineeringIndustries
Requirements
Candidates should possess a Bachelor’s or Master’s degree in Computer Science, Applied Math, Engineering, or a related technical field, demonstrating strong software engineering skills with experience in machine learning, numerical computing, and/or applied ML research. Practical experience deploying ML models or infrastructure in a research and production environment is required, along with familiarity with Python and numerical/ML frameworks such as PyTorch, JAX, and numpy, and a plus for experience with PyTorch 2.x compiler stack technologies. Understanding of GPU architecture and profiling techniques is also beneficial.
Responsibilities
The Research Engineer will scale neural network training systems to support larger, more complex models while maintaining the ability to run many experiments simultaneously, explore and prototype novel modeling approaches, collaborate with researchers to integrate promising models into production, profile and optimize distributed model training and inference utilizing GPU acceleration and ML compiler technologies, and establish best practices for a clean, reproducible, and maintainable research codebase.
Skills
Python
PyTorch
JAX
numpy
GPU architecture
Profiling techniques
Distributed model training
ML compiler technologies
Aquatic Capital Management
Quantitative investment management for institutional clients
About Aquatic Capital Management
Aquatic Capital Management specializes in quantitative investment management, focusing on creating financial models that predict market movements. The company uses advanced research and development techniques, employing a team of skilled researchers and engineers to develop data-driven investment strategies for institutional clients like hedge funds and pension funds. Aquatic's products work by leveraging mathematical models and algorithms to analyze market data and generate insights that help clients maximize their returns. Unlike many competitors, Aquatic operates without the limitations of outdated systems, allowing for greater agility and responsiveness to market changes. The company's goal is to build the best prediction machine in the industry while fostering a collaborative and innovative workplace culture that attracts top talent.
Chicago, IllinoisHeadquarters
2015Year Founded
VENTURE_UNKNOWNCompany Stage
Fintech, Quantitative FinanceIndustries
11-50Employees