Machine Learning Researcher

Capital Fund Management (CFM); Paris; On-site; EUR 90,000 - 130,000; Full time

Job summary

Capital Fund Management (CFM) seeks a Machine Learning Researcher to drive AI-based alpha models at the intersection of research and production engineering. You will design, implement, and scale models that power predictive signals, collaborating with data teams and quant researchers across the firm.

You will work with Python-based DL frameworks, distributed training tools, and state-of-the-art architectures, contributing to both research and engineering efforts in a fast-paced environment.

Qualifications

• PhD preferred in CS/ML or related quantitative field.
• 2–3 years AI research experience acceptable.
• Proven ability to develop end-to-end model pipelines.
• Experience with large-scale DL models and multi-GPU training.
• Strong coding and debugging skills in Python.

Responsibilities

• Contribute to AI-based alpha models across research and engineering.
• Iterate on data pipelines, target definitions, and training recipes.
• Develop and scale distributed training/evaluation workflows for large models.
• Collaborate with ML platform, data teams, and researchers across the firm.
• Share research findings and publish when possible.

Skills

Python

PyTorch

Distributed training

Deep learning

Profiling & debugging

Large codebases

Education

PhD in Computer Science/ML

2–3 years AI research experience

Tools

CUDA

Ray

Kubernetes

Megatron-LM

Job description

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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.

YOUR ROLE

We’re hiring for a Machine Learning Researcher: a role that blends modern ML research, GPU-heavy experimentation, quantitative modelling, and production-quality engineering. You’ll join a newly formed quant research team whose goal is to build AI-based alpha models that are predictive, reliable and efficient. You will work at the intersection of research and engineering to design, implement, and scale the AI models and algorithms that power our predictive signals. You’ll collaborate closely with data teams, the ML platform team, portfolio teams, and quant researchers across the firm.

YOUR RESPONSIBILITIES

• You’ll be engaged as a core contributor to our AI-based alpha models, working across research and engineering.
• Research: Iterate on data pipelines, target definitions, model architectures, and training recipes (optimization strategies and hyperparameters). You’ll be responsible for both getting things to work, and developing a deeper understanding, which we can bring to the next problem.
• Engineering: Develop, optimize, and scale distributed training and evaluation workflows for large-scale foundation models, working closely with the ML platform team to efficiently leverage multi-GPU infrastructure.
• Keep up with the latest DL research and collaborate with diverse teams, including other quant researchers, software engineers, and hardware architects.
• Attend conferences and communicate research results to the rest of the firm. Where possible, there will be the opportunity to publish your research.

YOUR SKILLS

Required:

• Proficiency in Python and at least one deep learning framework such as PyTorch.
• PhD in Computer Science, Machine Learning or other quantitative domains preferred. Open also to candidates with 2/3 years experience in a AI research role.
• Experience with end-to-end model development, spanning dataset construction, training, evaluation, profiling, and monitoring.
• Familiarity with modern model architectures, e.g. MoEs, long-context transformers, vision-language models, efficient attention mechanisms (e.g. GQA/MQA), and new techniques such as multi-token prediction.
• Experience training and scaling models using distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP, or Megatron-LM.
• Strong engineering skills, ability to contribute performant & maintainable code, profile bottlenecks, debug training failures, and work with large codebases.
• Initiative and appetite for helping shape the direction of a newly formed team.

Preferred (we encourage you to apply even if you don’t satisfy all of the below):

• Experience optimizing LLM inference (e.g. KV-cache management, continuous batching, quantization, speculative decoding)
• Familiarity with distributed execution and orchestration tools such as Ray or Kubernetes.
• Experience with RLHF, RLAIF, DPO, or reward modeling.
• Experience with CUDA, including developing custom kernels or other GPU performance optimizations.

EQUAL OPPORTUNITIES STATEMENT

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 .