Founded in 1991, we are a global quantitative and systematic asset management firm applying a scientific approach to finance to develop alternative investment strategies that create value for our clients.
We value innovation, dedication, collaboration, and the ability to make an impact. Together, we create a stimulating environment for talented and passionate experts in research, technology, and business to explore new ideas and challenge existing assumptions.

Summary

We're seeking an ML Research Intern to develop and evaluate modern sequence modeling architectures for time series prediction. You'll design systematic experiments, implement novel architectural components, and build benchmarking frameworks to compare design choices across multiple evaluation criteria.

Key Responsibilities
- Implement and systematically compare transformers and other modern sequence architectures
- Design low-level architectural components: tokenization schemes, positional encodings, attention mechanisms
- Analyze model behavior through statistical measures: temporal structure preservation, distribution matching, predictive performance
- Optimize computational efficiency and study parameter utilization patterns

Required Qualifications
- Strong expertise in modern ML architectures, particularly transformers and attention mechanisms
- Deep understanding of sequence modeling fundamentals and architectural design choices
- Proficient in PyTorch/JAX with experience implementing custom architectures
- Solid background in time series analysis and statistical modeling
- Experience with systematic experimental design and ablation studies
- Strong problem-solving skills and ability to work independently on research problems

Preferred Qualifications
- Experience with efficient sequence modeling architectures beyond standard transformers
- Knowledge of information theory and statistical distance measures
- Background in GPU optimization and computational efficiency analysis
- Experience with systematic benchmarking and reproducible research practices
- Understanding of temporal modeling challenges and solutions 

We are continuously striving to be an equal opportunity employer and we prohibit any discrimination based on sex, disability, origin, sexual orientation, gender identity, age, race, or religion. We believe that our diversity, breadth of experience, and multiple points of view are among the leading factors in our success.
CFM is a signatory of the Women Empowerment Principles.
 

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Profile description:

Required Qualifications
- Strong expertise in modern ML architectures, particularly transformers and attention mechanisms
- Deep understanding of sequence modeling fundamentals and architectural design choices
- Proficient in PyTorch/JAX with experience implementing custom architectures
- Solid background in time series analysis and statistical modeling
- Experience with systematic experimental design and ablation studies
- Strong problem-solving skills and ability to work independently on research problems

Preferred Qualifications
- Experience with efficient sequence modeling architectures beyond standard transformers
- Knowledge of information theory and statistical distance measures
- Background in GPU optimization and computational efficiency analysis
- Experience with systematic benchmarking and reproducible research practices
- Understanding of temporal modeling challenges and solutions