Mistral AI
Research Engineer, Machine Learning
Palo Alto
Sponsorship not specifiedDetected 177 days ago
AlgorithmsCode ReviewKubernetesMachine LearningDeep LearningTensorFlowPyTorchData EngineeringNLPLLMsResearchMentoring
About the role
- spans Platform (shared infra & clean code) and Embedded (inside research squads).
- Engineers can move along the research↔production spectrum as needs or interests evolve.
Responsibilities
- Our technology is designed to integrate seamlessly into daily working life.
Requirements
- Master's or PhD in Computer Science (or equivalent proven track record).
Skills
- Enhance the shared training framework, data pipelines and cluster tooling used by every team; or
Compensation
- 💰 Competitive salary and equity. 🚑 Healthcare: Medical/Dental/Vision covered for you and your family. 👴🏻 Pension: 401K (6% matching) 🏝️ PTO: 18 days 🚗 Transportation: Reimburse office parking charges, or $120/month for public transpor
Benefits
- Experience in deep learning, NLP or LLMs; bonus for CUDA or data-pipeline chops.
- Medical/Dental/Vision covered for you and your family. 👴🏻
- At Mistral AI, we believe in the power of AI to simplify tasks, save time, and enhance learning and creativity.
Company info
- Our comprehensive AI platform is designed to meet enterprise as well as personal needs.
- We are a dynamic, collaborative team passionate about AI and its potential to transform society.
- We are creative, low-ego and team-spirited.
- Join us to be part of a pioneering company shaping the future of AI.
- See more about our culture on https://mistral.ai/careers.
Equal opportunity
- Our diverse workforce thrives in competitive environments and is committed to driving innovation.
Visa & Work Authorization
- 💰 Competitive salary and equity. 🚑 Healthcare: Medical/Dental/Vision covered for you and your family. 👴🏻 Pension: 401K (6% matching) 🏝️ PTO: 18 days 🚗 Transportation: Reimburse office parking charges, or $120/month for public transpor
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This listing is sourced directly from Mistral AI's careers page and normalized into a canonical job model.