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- Tasks: Join a dynamic team to build and maintain data pipelines for AI research.
- Company: Russell Tobin connects top talent with innovative companies in tech.
- Benefits: Enjoy hybrid work flexibility and collaborate with leading researchers.
- Why this job: Make an impact on cutting-edge AI projects and enhance your skills.
- Qualifications: 2-6 years in data engineering, strong Python skills, and problem-solving abilities required.
- Other info: Opportunity to work with large datasets and contribute to intelligent systems.
The predicted salary is between 44000 - 66000 £ per year.
Direct message the job poster from Russell Tobin
Senior Associate | Talent Acquisition Specialist | Connecting Companies with Exceptional Professionals | Certified LinkedIn Recruiter
Location: London, UK (Hybrid – 3 days/week onsite at Kings Cross)
About the Role
We are seeking a talented Software Engineer / Data Engineer with strong experience in building and maintaining data pipelines, specifically for AI and machine learning research projects. You will be joining a cutting-edge research team developing intelligent AI agents, working with large datasets that include text, images, and videos.
Your work will be focused on data onboarding, cleaning, filtering, and transformation – ensuring datasets are safe, high-quality, and ready for use in advanced research. You will collaborate closely with other engineers and researchers to automate processes, enhance data pipelines, and contribute to the success of high-impact AI initiatives.
Key Responsibilities
- Data Onboarding & Preprocessing: Convert original datasets into a usable format for filtering pipelines.
- Filtering & Cleaning: Apply data quality checks and filtering processes to text, image, and video datasets.
- Post-processing: Repackage approved datasets for reintegration into AI research workflows.
- Pipeline Development: Build, maintain, and improve Python-based data pipelines (ETL workflows).
- Collaboration: Work with researchers and engineering peers on automation solutions and risk mitigation.
- Quality Assurance: Ensure datasets meet high standards for accuracy, privacy, and usability.
Required Skills & Experience
- Professional Experience: 2–6 years as a Data Engineer, Software Engineer, or similar.
- Strong Python programming skills with proven experience in data pipelines and ETL workflows .
- Practical experience with data cleaning, transformation, and quality control .
- Knowledge of data storage solutions and large dataset management.
- Strong problem-solving skills and ability to work in a fast-paced research environment .
Nice-to-Have Skills
- Experience with machine learning frameworks (e.g., PyTorch).
- Background in big tech or AI/ML research environments.
Why Join Us?
- Opportunity to work directly on cutting-edge AI research projects with global impact.
- Collaborative team environment with research scientists and engineers.
- Be a core contributor to automation processes powering the next generation of intelligent systems.
- Hybrid working setup – flexibility plus the benefits of in-person collaboration.
Seniority level
-
Seniority level
Not Applicable
Employment type
-
Employment type
Contract
Job function
-
Job function
Information Technology
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Industries
Social Networking Platforms and IT Services and IT Consulting
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Software Engineer (Data Engineering/AI Research) employer: Russell Tobin
Contact Detail:
Russell Tobin Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Software Engineer (Data Engineering/AI Research)
✨Tip Number 1
Make sure to showcase your experience with Python and data pipelines in your conversations. Highlight specific projects where you've built or maintained ETL workflows, as this will resonate well with the team.
✨Tip Number 2
Familiarise yourself with the latest trends in AI and machine learning. Being able to discuss recent advancements or frameworks like PyTorch can set you apart during discussions with the hiring team.
✨Tip Number 3
Network with current employees or alumni from your university who work in similar roles. They can provide insights into the company culture and expectations, which can be invaluable during your interactions.
✨Tip Number 4
Prepare to discuss your problem-solving skills in a fast-paced environment. Think of examples where you've successfully tackled challenges in data cleaning or pipeline development, as these will demonstrate your fit for the role.
We think you need these skills to ace Software Engineer (Data Engineering/AI Research)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data engineering and AI research. Focus on your Python programming skills, data pipeline development, and any experience with ETL workflows. Use specific examples to demonstrate your problem-solving abilities.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Mention your experience with data onboarding, cleaning, and transformation, and how it aligns with the responsibilities outlined in the job description. Show that you understand the importance of high-quality datasets in AI research.
Highlight Collaboration Skills: Since the role involves working closely with researchers and engineers, emphasise your teamwork and collaboration skills. Provide examples of past projects where you successfully collaborated with others to achieve common goals, particularly in fast-paced environments.
Showcase Relevant Projects: If you have worked on any projects related to machine learning or data engineering, be sure to include them in your application. Describe your role, the technologies used, and the impact of the project. This will help demonstrate your practical experience and passion for the field.
How to prepare for a job interview at Russell Tobin
✨Showcase Your Python Skills
Since strong Python programming skills are crucial for this role, be prepared to discuss your experience with Python in detail. Bring examples of data pipelines or ETL workflows you've built, and be ready to explain the challenges you faced and how you overcame them.
✨Demonstrate Data Quality Awareness
Highlight your understanding of data cleaning, filtering, and quality control processes. Discuss specific techniques you've used to ensure high-quality datasets, as this is a key responsibility in the role. Be ready to share examples of how you've handled data quality issues in past projects.
✨Prepare for Collaboration Questions
Collaboration is essential in this position, so expect questions about your experience working with researchers and engineers. Think of examples where you successfully collaborated on projects, particularly in automating processes or enhancing data pipelines.
✨Familiarise Yourself with AI Research
Since you'll be working on AI and machine learning research projects, it’s beneficial to have a basic understanding of these fields. Brush up on relevant concepts and be prepared to discuss how your work can contribute to intelligent systems and automation in AI research.
