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- Tasks: Own and enhance the post-training pipeline for our AI product.
- Company: Lovable, a forward-thinking tech company in Greater London.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Collaborative environment focused on improving user experiences.
- Why this job: Join us to translate cutting-edge research into impactful AI solutions.
- Qualifications: Experience with large language models and production coding required.
The predicted salary is between 60000 - 80000 £ per year.
Lovable in Greater London is seeking an Engineer to own and enhance the post-training pipeline for its AI product. This role involves translating research into production, developing robust models, and ensuring rapid deployment to users.
Candidates should have experience with large language models, be proficient in production coding, and understand evaluation methodologies. Strong collaboration and problem-solving skills are essential as you'll work across teams to improve user experiences and systems.
Production AI Researcher: Post-Training & CodeGen employer: Lovable
Contact Detail:
Lovable Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Production AI Researcher: Post-Training & CodeGen
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and tech community, especially those who work at Lovable or similar companies. 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 GitHub with projects related to large language models or production coding, make sure to highlight them. We love seeing practical examples of your work!
✨Tip Number 3
Prepare for the interview by brushing up on evaluation methodologies and post-training processes. We want to see how you think and solve problems, so be ready to discuss your approach to enhancing AI products.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you're genuinely interested in joining our team at Lovable.
We think you need these skills to ace Production AI Researcher: Post-Training & CodeGen
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with large language models and production coding. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re excited about the role and how your background makes you a perfect fit. We love seeing genuine enthusiasm for what we do at StudySmarter.
Showcase Collaboration Skills: Since this role involves working across teams, make sure to mention any past experiences where you’ve successfully collaborated with others. We value strong teamwork, so let us know how you’ve contributed to improving user experiences.
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 don’t miss out on any important updates from our team!
How to prepare for a job interview at Lovable
✨Know Your AI Models
Make sure you brush up on your knowledge of large language models. Be ready to discuss how you've worked with them in the past, and think about specific examples where you've translated research into production. This will show that you understand the technical side of the role.
✨Showcase Your Coding Skills
Since production coding is a key part of this job, be prepared to demonstrate your coding abilities. Bring along examples of your work or be ready to talk through your coding process. Familiarise yourself with the tools and languages commonly used in AI production to impress your interviewers.
✨Collaboration is Key
This role requires strong collaboration skills, so think of examples where you've successfully worked across teams. Be ready to discuss how you approach problem-solving in a team setting and how you’ve contributed to improving user experiences in previous projects.
✨Understand Evaluation Methodologies
Brush up on evaluation methodologies relevant to AI products. Be prepared to discuss how you measure the success of your models and what metrics you consider important. This will demonstrate your analytical skills and your commitment to delivering high-quality results.