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- Tasks: Review and audit code annotations to ensure high-quality data for AI models.
- Company: Join Mistral, a dynamic team on a mission to democratise AI.
- Benefits: Competitive salary, equity, health insurance, and gym contributions.
- Other info: Hybrid role with opportunities for professional growth and collaboration.
- Why this job: Make a real impact in AI while working with cutting-edge technology.
- Qualifications: Degree in computer science or relevant experience in software engineering.
The predicted salary is between 30000 - 40000 £ per year.
About Mistral
At Mistral we are on a mission to democratize AI, producing frontier intelligence for everyone, developed in the open, and built by engineers all over the world. 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, with teams distributed between Europe, the USA and Asia.
Role Summary
We’re seeking highly motivated Data Quality Specialists with strong analytical skills and a keen eye for detail to join our Human Data Annotation team within the Science organisation. This is a hybrid quality reviewing and tooling role: you'll spend the majority of your time reviewing and auditing code annotations against rubrics to ensure data used for training and evaluating AI models meets a high bar, and the remainder building, maintaining, and troubleshooting the internal tooling that annotators rely on day-to-day. You’ll collaborate closely with the annotators, technical program manager, and engineer stakeholders, and contribute to refining the guidelines and processes that shape how our data is produced.
Key Responsibilities
- Generate and validate high-quality data annotations, based on guidelines and continuous feedback, for the development and evaluation of AI models.
- Surface systemic issues, edge cases, and gaps in guidelines back to annotation operations and technical stakeholders.
- Produce annotations yourself when needed, modeling the quality bar expected of the team.
- Build and maintain internal tools and automation that streamline annotator workflows such as visualization dashboards, batch configuration scripts, output management utilities, and similar.
- Troubleshoot environment, tooling, and CLI/git issues for annotators on their local machines, liaising with IT and engineering as needed.
About You
- A degree in computer science, engineering, or a related field. Alternatively, 2 to 5 years of professional experience in software engineering, technical support, or developing tools.
- Hands‑on experience using code agents (e.g. Mistral’s vibe) in your own development workflow, and genuine interest in how they're evolving.
- Proficient in at least one programming language (e.g. Python, JavaScript, or similar), with enough breadth to read and reason about code across a few core languages.
- Able to apply consistent judgment against a rubric and surface edge cases, ambiguities, or gaps in guidelines.
- Sustained focus and accuracy on detail‑oriented, high‑volume review work.
- Comfortable working in a Unix‑like terminal: shell basics, package managers, environment setup, and git workflows (branches, merges, resolving conflicts).
- Able to troubleshoot local development environment issues (dependencies, virtual environments, paths, permissions) across common operating systems.
- Professional proficiency in English, with strong writing and comprehension skills.
Nice to have
- Prior experience in data annotation for AI/ML, especially LLM training (SFT, RLHF, preference data), evals/benchmarks, or agentic data.
- Experience building an annotation team through interviews and training.
- Experience supporting technical users or troubleshooting developer environments (internal tools support, DevRel, teaching assistant for coding courses, etc.).
- Fluency across multiple programming languages, or domain depth in one of: frontend, backend, DevOps, MLOps, data engineering.
- Familiarity with rubric‑based evaluation concepts, inter‑annotator agreement, or quality measurement for human‑labeled data.
- Experience developing, deploying, and managing internal tooling or automation scripts.
Benefits
- Competitive cash salary and equity.
- Food: Daily lunch vouchers.
- Sport: Monthly contribution to a Gympass subscription.
- Transportation: Monthly contribution to a mobility pass.
- Health: Full health insurance for you and your family.
- Parental: Generous parental leave policy.
- Visa sponsorship.
Code Data Annotation Quality Specialist employer: Mistral AI
Contact Detail:
Mistral AI Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Code Data Annotation Quality Specialist
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and data annotation space on LinkedIn or at meetups. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your coding projects or any data annotation work you've done. This gives potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on common questions related to data quality and coding. Practice explaining your thought process when troubleshooting issues, as this is key for roles like the one at Mistral.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Code Data Annotation Quality Specialist
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the Code Data Annotation Quality Specialist role. Highlight your relevant experience in data annotation, programming skills, and any tools you've worked with that align with what we’re looking for.
Show Off Your Skills: Don’t just list your skills; demonstrate them! If you’ve got hands-on experience with coding or troubleshooting, share specific examples in your application. We want to see how you’ve tackled challenges in the past.
Be Clear and Concise: When writing your application, keep it straightforward. Use clear language and avoid jargon unless it’s relevant. We appreciate a well-structured application that gets straight to the point!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re genuinely interested in joining our team!
How to prepare for a job interview at Mistral AI
✨Know Your Code
Make sure you brush up on your programming skills, especially in languages like Python or JavaScript. Be ready to discuss your experience with code agents and how you've used them in your workflow. This will show that you’re not just familiar with the tools but can also apply them effectively.
✨Understand the Role
Familiarise yourself with the responsibilities of a Data Quality Specialist. Think about how you would generate and validate high-quality data annotations and be prepared to discuss any relevant experiences. Showing that you understand the nuances of the role will set you apart.
✨Prepare for Technical Questions
Expect questions about troubleshooting local development environments and using Unix-like terminals. Brush up on your knowledge of git workflows and be ready to explain how you would handle common issues. This demonstrates your technical proficiency and problem-solving skills.
✨Show Your Collaborative Spirit
Since this role involves working closely with annotators and technical stakeholders, be prepared to discuss your teamwork experiences. Share examples of how you've collaborated in the past, especially in high-pressure situations, to highlight your ability to thrive in a dynamic environment.