At a Glance
- 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 innovation.
- Why this job: Make a real impact in AI while collaborating with a global team.
- Qualifications: Degree in computer science or 2-5 years of relevant experience.
The predicted salary is between 50000 - 70000 £ 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.
- Daily lunch vouchers.
- Monthly contribution to a Gympass subscription.
- Monthly contribution to a mobility pass.
- Full health insurance for you and your family.
- Generous parental leave policy.
- Visa sponsorship.
Code Data Quality Specialist in London employer: Mistral AI
Mistral AI is an exceptional employer, offering a dynamic work environment in Greater London where innovation meets collaboration. With competitive salaries, equity options, and comprehensive health benefits, we prioritise employee well-being and growth, fostering a culture that encourages continuous learning and development in the exciting field of multilingual AI research.
StudySmarter Expert Advice🤫
We think this is how you could land Code Data Quality Specialist in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Mistral. A friendly chat can open doors and give you insights that a job description just can't.
✨Tip Number 2
Show off your skills! If you've got a portfolio or some projects that highlight your coding prowess, make sure to share them during interviews. It’s a great way to demonstrate your analytical skills and attention to detail.
✨Tip Number 3
Prepare for technical questions! Brush up on your programming languages and be ready to discuss how you've tackled challenges in your past roles. This will show you're not just a fit on paper but in practice too.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in being part of the Mistral team.
We think you need these skills to ace Code Data Quality Specialist in London
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter for the Code Data Quality Specialist role. Highlight your analytical skills and any relevant experience with data annotation or coding. We want to see how you fit into our mission at Mistral!
Show Off Your Skills:Don’t just list your qualifications; demonstrate them! Include specific examples of projects where you've used programming languages or tools relevant to the role. This helps us see your hands-on experience in action.
Be Clear and Concise:When writing your application, keep it straightforward and to the point. Use clear language and avoid jargon unless it's necessary. We appreciate clarity as much as we value detail!
Apply Through Our Website:We encourage you to submit your application through our website. It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it’s super easy!
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
Dive deep into the job description and understand the key responsibilities. Be prepared to talk about your experience with data annotation and how you’ve ensured quality in previous roles. Highlight any specific projects where you’ve had to troubleshoot or build internal tools.
✨Show Your Analytical Skills
Since this role requires a keen eye for detail, be ready to demonstrate your analytical skills. You might be asked to evaluate sample annotations or identify edge cases. Practise articulating your thought process clearly, as this will showcase your ability to apply consistent judgment against rubrics.
✨Prepare for Technical Questions
Expect some technical questions related to Unix-like environments and git workflows. Brush up on shell basics and be ready to discuss how you’ve resolved conflicts or managed dependencies in your development environment. This will help you stand out as someone who can support annotators effectively.
