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- Tasks: Build and maintain Python-based data pipelines for AI research.
- Company: Join Principle, a leading tech firm at the forefront of AI innovation.
- Benefits: Enjoy a hybrid work model, competitive salary, and weekly pay.
- Why this job: Dive into real ML projects and collaborate with top researchers in a dynamic environment.
- Qualifications: 3-6 years in software/data engineering with strong Python skills required.
- Other info: Contract until December 2025 with potential for extension.
The predicted salary is between 54000 - 126000 £ per year.
This range is provided by Principle. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base pay range
Direct message the job poster from Principle
Technology & Engineering Division Senior Advisor
Python Engineer – Data Pipelines & ML (Contract to Dec 2025)
Looking to sharpen your machine learning experience and get close to real AI research? Join a fast-moving engineering team working on the data backbone for advanced LLM agents. You’ll build and run pipelines that onboard and filter large datasets (text, image, video) to support one of the leading AI research labs.
What’s in it for you?
- Annual Salary up to £90,000 doe
- 3 days onsite – hybrid work model – non-negotiable
- Location: Central London
- Contract until end of the year 2025 (inside IR35) – PAYE – paid on a weekly basis + strong possibility of extension.
- Work directly with researchers and engineers, shaping AI automation
- Build practical ML tooling—not just notebooks or dashboards
What you’ll be doing:
- Build and maintain Python-based data pipelines (ETL, cleaning, filtering)
- Preprocess and transform large datasets used in ML research
- Filter datasets for risks and quality issues
- Collaborate closely with the data and ML teams (including FAIR)
You should have:
- 3–6 years’ experience in full stack software/data engineering
- Strong Python coding skills (ideally with Pandas, NumPy, FastAPI or similar)
- Experience with data pipelines and working with unstructured data (text/image/video)
- Exposure to machine learning workflows
- PyTorch experience is a plus, not a blocker
We’re looking for someone hands-on, curious, and solid with Python—not a PhD, not a team lead. If you’re eager to get deeper into ML/AI work and build things that actually get used, this could be your next move.
Apply Now with your CV or your LinkedIn profile. Som |
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Python Engineer – Data Pipelines & ML - Global Tech employer: Principle
Contact Detail:
Principle Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Python Engineer – Data Pipelines & ML - Global Tech
✨Tip Number 1
Familiarise yourself with the specific tools mentioned in the job description, such as Pandas, NumPy, and FastAPI. Having hands-on experience with these libraries will not only boost your confidence but also demonstrate your readiness to hit the ground running.
✨Tip Number 2
Engage with the AI and ML community online. Join forums, attend webinars, or participate in relevant discussions on platforms like GitHub or Stack Overflow. This will help you stay updated on industry trends and may even lead to valuable connections.
✨Tip Number 3
Prepare to discuss your previous projects involving data pipelines and machine learning. Be ready to explain your role, the challenges you faced, and how you overcame them. This will showcase your practical experience and problem-solving skills.
✨Tip Number 4
Research the company and its current projects in AI and ML. Understanding their goals and challenges will allow you to tailor your conversations during interviews, showing that you're genuinely interested in contributing to their success.
We think you need these skills to ace Python Engineer – Data Pipelines & ML - Global Tech
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your Python coding skills and experience with data pipelines. Include specific projects or roles where you've worked with unstructured data and machine learning workflows.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the opportunity to work on AI research. Mention how your hands-on experience aligns with the job requirements and your eagerness to contribute to the team.
Showcase Relevant Projects: If you have any relevant projects, especially those involving ETL processes or machine learning, be sure to include them in your application. This can demonstrate your practical skills and understanding of the field.
Highlight Collaboration Skills: Since the role involves working closely with researchers and engineers, emphasise any past experiences where you've collaborated in a team setting. This will show that you're a good fit for their fast-moving engineering team.
How to prepare for a job interview at Principle
✨Showcase Your Python Skills
Make sure to highlight your strong Python coding skills during the interview. Be prepared to discuss specific projects where you've used libraries like Pandas, NumPy, or FastAPI, and how they contributed to building data pipelines.
✨Demonstrate Your Experience with Data Pipelines
Discuss your hands-on experience with building and maintaining data pipelines. Be ready to explain the ETL processes you've implemented and how you've handled unstructured data, as this is crucial for the role.
✨Familiarise Yourself with Machine Learning Workflows
Since the position involves working closely with ML teams, brush up on your knowledge of machine learning workflows. Be prepared to talk about any relevant experience you have, even if it's not extensive, and express your eagerness to learn more.
✨Prepare Questions for the Interviewers
Have a few thoughtful questions ready to ask your interviewers. This shows your interest in the role and the company. You might want to inquire about the types of datasets you'll be working with or how the team collaborates on projects.
