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Applied Machine Learning Engineer, Core Models
Summary by AIEngineer.careers
Applied Machine Learning Engineer, Core Models is a Machine Learning Engineer role (full-time). with Mistral AI. in PARIS, FR. Compensation shown: $130K–$216K (est.). Imported listing (source: aibreakingwire.com). Apply on the employer's site (aibreakingwire.com).
Imported listing (source: aibreakingwire.com) · Apply on aibreakingwire.com
PARIS · FR
$130K–$216K (est.)
Full-time
MID
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from aibreakingwire.com
Mistral AI is seeking an Applied Machine Learning Engineer to join the Core Models team in Paris or remotely. You will train, fine-tune, and deploy open and commercial foundation models, focusing on efficient model architecture and multilingual processing. Collaborate with enterprise customers and product teams to integrate customized models into production.
Responsibilities
- Train, fine-tune, and evaluate state-of-the-art open-weights foundation models.
- Optimize model efficiency for downstream enterprise inference and edge deployment.
- Design rigorous data pipelines, curation strategies, and filtering techniques for pre-training corpora.
- Work with enterprise customers and product teams to integrate customized models into production environments.
Requirements
- Master's or Ph.D. in Computer Science or related quantitative discipline.
- 3+ years of experience training and deploying deep learning models at scale.
- Mastery of Python, PyTorch, and modern LLM tooling (transformers, vLLM, DeepSpeed).
- Strong background in transformer architectures, quantization, and parameter-efficient fine-tuning.
Benefits
Mistral AI
Paris · FR · 1019+ employees
Mistral AI is a French artificial-intelligence company founded in 2023 and headquartered in Paris. It develops open-weight and commercial large language models and related generative-AI products, with a mission to make frontier AI broadly accessible.
