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- Tasks: Build systems and pipelines to enhance data quality for AI evaluation.
- Company: Leading tech firm in Greater London with a focus on innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Why this job: Make a direct impact on data quality in a high-impact team environment.
- Qualifications: 3+ years in software engineering, proficient in Python and SQL.
- Other info: Experience with big data systems and AWS is essential.
The predicted salary is between 60000 - 80000 £ per year.
A leading tech firm in Greater London is seeking a Software Engineer to build systems and pipelines that enhance data quality. The role requires over 3 years of experience in software engineering, particularly with Python and SQL. You will have the opportunity to shape how the company measures and improves answer quality, making a direct impact in a high-impact team environment.
Experience with big data systems and AWS is essential, with familiarity in coding workflows being a plus.
Data Flywheel Engineer for AI Eval Pipelines employer: Perplexity
Contact Detail:
Perplexity Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Flywheel Engineer for AI Eval Pipelines
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those who work at the company you're eyeing. A friendly chat can sometimes lead to insider info or even a referral, which can give you a leg up in the application process.
✨Tip Number 2
Show off your skills! If you've got a GitHub or portfolio showcasing your Python and SQL projects, make sure to highlight that. It’s a great way to demonstrate your experience and passion for software engineering without just relying on your CV.
✨Tip Number 3
Prepare for the interview by brushing up on big data systems and AWS. We recommend doing some mock interviews with friends or using online platforms to get comfortable discussing your technical expertise and how it relates to enhancing data quality.
✨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 take that extra step to connect with us directly.
We think you need these skills to ace Data Flywheel Engineer for AI Eval Pipelines
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python, SQL, and any big data systems you've worked with. We want to see how your skills align with the role of a Data Flywheel Engineer, so don’t hold back on showcasing relevant projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about enhancing data quality and how your background makes you a perfect fit for our team. We love seeing enthusiasm and a personal touch!
Showcase Your Impact: When detailing your past experiences, focus on the impact you made in previous roles. Did you improve a process or enhance data quality? We want to hear about it! Numbers and specific examples can really make your application stand out.
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’s super easy – just follow the prompts and let us know why you’d be a great fit!
How to prepare for a job interview at Perplexity
✨Know Your Tech Stack
Make sure you brush up on your Python and SQL skills before the interview. Be ready to discuss specific projects where you've used these technologies, as well as any challenges you faced and how you overcame them.
✨Showcase Your Big Data Experience
Since experience with big data systems is essential, prepare examples of how you've worked with large datasets. Discuss the tools and frameworks you've used, and be ready to explain how you ensured data quality in those scenarios.
✨Familiarise Yourself with AWS
If you have experience with AWS, make sure to highlight it. Be prepared to talk about specific services you've used, such as S3 or EC2, and how they contributed to your projects. If you're not familiar, do some research on common AWS services related to data engineering.
✨Prepare for Coding Workflows
Since familiarity with coding workflows is a plus, think about how you've implemented or improved workflows in your previous roles. Be ready to discuss any tools or methodologies you've used, like CI/CD pipelines, and how they helped streamline processes.