JobgetherRemote · Canada
1h ago
Mistral AIPosted 1w ago
L4
Applied AI, Forward Deployed Machine Learning Engineer - (Internship)
Applied AI, Forward Deployed Machine Learning Engineer - (Internship) at Mistral AI scores 94 out of 100 on AI centrality, which makes it a Level 4 role on this board.
ParisinternIntern
AI in this role
pytorch
ml-opsnlp
About Mistral
Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms.
We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited.
The Role
As an Applied Engineering Intern, you will work closely with our Applied AI Engineering team to facilitate the adoption of Mistral AI products among customers and collaborate with them to address complex technical challenges. This role is based in Paris, with an internship duration of 6 months. We are open to CIFRE programs as a continuation after the internship.
What you will do
• Contribute to the deployment of state-of-the-art GenAI applications, driving technological transformation with our customers.
• Collaborate with researchers, AI engineers, and product engineers on complex customer projects.
• Work with the product and science team to continuously improve our product and model capabilities based on customer feedback.
How We Work in Applied AI
- We care about people and outputs.
- What matters is what you ship, not the time you spend on it
- Bureaucracy is where urgency goes to vanish. You talk to whoever you need to talk to. The best idea wins, whether it comes from a principal engineer or someone in their first week.
- Always ask why. The best solutions come from deep understanding, not from copying what worked before
- We say what we mean. Feedback is direct, timely, and given because we care.
- No politics. Low ego, high standards.
- We embrace an unstructured environment and find joy in it.
About you
• You are currently pursuing a degree in AI, data science, or a related field from a tier 1 engineering school or university.
• You have strong programming skills in Python.
• You are familiar with machine learning algorithms and natural language processing techniques.
• You hold basic understanding of MLOps and deploying machine learning use cases.
• You have good communication skills with the ability to explain technical concepts to both technical and non-technical audiences.
Ideally you have:
• Experience with deep learning frameworks such as PyTorch.
• Familiarity with version control systems (e.g., Git) and Linux shell environment.
• Experience working in HPC Environments
• Publication record in AI or a related field
Location & work authorization: This internship is available only in France. Please apply only if you have the required work authorization to complete the internship in the relevant country for its full duration.
What We Offer
We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks.
For the most up-to-date details on benefits available in your location, please refer to our Benefits page.
Privacy Policy
Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Ml OpsNlpPyTorch
Questions you could be asked
- How do you monitor a model once it's live, and how do you know it needs retraining?
- What NLP problem have you worked on, and how did you measure whether it actually worked?
- What are the limits of PyTorch that you've run into, and how did you work around them?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: Ml Ops, Nlp, and PyTorch. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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