At a Glance
- Tasks: Lead the open-sourcing of cutting-edge ML models and enhance Mistral's libraries.
- Company: Join a forward-thinking tech company focused on open-source innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative team environment with dynamic project involvement and career advancement.
- Why this job: Make a real impact in the open-source community while working with advanced ML technologies.
- Qualifications: Master’s degree in relevant fields and experience with popular open-source libraries.
The predicted salary is between 60000 - 80000 € per year.
Requirements
- Master’s degree in Computer Science, Machine Learning, Data Science, or a related field
- Experience contributing to popular open-source libraries such as PyTorch, Tensorflow, JAX, vLLM, Transformers, Llama.cpp
- Passion for contributing to the open-source software ecosystem
- Expert programming skills in Python, PyTorch, MLOps
- Adaptable, proactive, and autonomous
- Attention to detail and a drive to go the last mile to build almost perfect tools
- Deep understanding of machine learning approaches, especially LLMs and algorithms
- Low-ego, collaborative and have a real team player mindset
- (Desirable) Experience with training and fine-tuning large language models (e.g., distillation, supervised fine-tuning, policy optimization)
- (Desirable) Experience working with Slurm
- (Desirable) Worked with research teams before
- (Desirable) Experience as a core-maintainer of a popular ML open-source library
What the job involves
- You will be in charge of open-sourcing state-of-the-art models, whilst maintaining and improving Mistral’s publicly available libraries.
- Your work is critical in helping turn research breakthroughs into tangible solutions and improve Mistral's open-source ecosystem.
- Our OSS team is embedded in our Science team and works very closely with various engineering and marketing teams.
- All OSS team members can fluidly move on the production/research spectrum depending on where the needs are or where their interests lie.
- Releasing our models to open-source platforms and libraries, e.g., vLLM, GitHub, Hugging Face.
- Maintaining Mistral’s open-source libraries (mistral-common, mistral-finetune, mistral-inference).
- Create and maintain tooling and services: both internal facing (internal research) and external facing (open-source libraries).
- Implement and optimize open-source and internal libraries for performance and accuracy, ensuring production readiness and employing cutting-edge technology and innovative approaches.
- Collaborate with the open-source community (PyTorch, vLLM, Hugging Face).
Machine Learning Engineer (Open-Source Software) employer: Deepstreamtech
Mistral is an exceptional employer for Machine Learning Engineers, offering a dynamic work environment that fosters innovation and collaboration. With a strong emphasis on open-source contributions, employees have the unique opportunity to work on cutting-edge projects while engaging with a vibrant community. The company promotes professional growth through diverse roles within the OSS and Science teams, ensuring that every team member can thrive and make a meaningful impact in the rapidly evolving field of machine learning.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer (Open-Source Software)
✨Tip Number 1
Network like a pro! Reach out to folks in the open-source community, especially those involved with libraries like PyTorch and TensorFlow. Join forums, attend meetups, and don’t be shy about sharing your passion for machine learning.
✨Tip Number 2
Show off your skills! Contribute to open-source projects that excite you. Whether it’s fixing bugs or adding features, this not only builds your portfolio but also gets you noticed by potential employers who value hands-on experience.
✨Tip Number 3
Tailor your approach! When reaching out to companies, including us at StudySmarter, highlight your relevant experience with large language models and any collaborative projects. Make it clear how you can contribute to their open-source ecosystem.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive and genuinely interested in joining our team.
We think you need these skills to ace Machine Learning Engineer (Open-Source Software)
Some tips for your application 🫡
Show Your Passion for Open-Source:When writing your application, let us see your enthusiasm for open-source software. Share any contributions you've made to popular libraries like PyTorch or TensorFlow, and explain why you love being part of the open-source community.
Highlight Relevant Experience:Make sure to detail your experience with machine learning and programming in Python. If you've worked on training large language models or have been a core maintainer of an ML library, shout about it! We want to know how your skills align with our needs.
Be Clear and Concise:Keep your application straightforward and to the point. Use clear language and avoid jargon unless it's relevant. We appreciate attention to detail, so make sure your application is well-structured and free of typos.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you're proactive, which we love!
How to prepare for a job interview at Deepstreamtech
✨Know Your Open-Source Stuff
Make sure you brush up on your experience with popular open-source libraries like PyTorch and TensorFlow. Be ready to discuss specific contributions you've made, as well as any challenges you faced while working on these projects.
✨Show Off Your Collaboration Skills
Since teamwork is key in this role, think of examples where you've successfully collaborated with others. Highlight your low-ego approach and how you’ve contributed to a team environment, especially in research settings.
✨Demonstrate Your Technical Expertise
Prepare to dive deep into your programming skills, particularly in Python and MLOps. You might be asked to solve a coding problem or explain your thought process behind a machine learning model you've worked on, so practice articulating your technical knowledge.
✨Be Ready for the Last Mile
This role requires attention to detail and a drive to perfect tools. Think of instances where you went above and beyond to ensure quality in your work. Be prepared to discuss how you approach fine-tuning models and ensuring they are production-ready.
