At a Glance
- Tasks: Design and build AI systems that transform marketing intelligence and automate decision-making.
- Company: Join Rockerbox, a leader in innovative marketing technology.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with mentorship opportunities and cutting-edge projects.
- Why this job: Make a real impact by optimising AI for complex marketing challenges.
- Qualifications: 8+ years in data engineering with hands-on LLM integration experience.
The predicted salary is between 60000 - 84000 £ per year.
Rockerbox is building the next generation of marketing intelligence, and we’re looking for someone to help us build the AI systems everyone else just theorizes about. As a Staff AI Data Engineer, you’ll research, design, and architect data systems purpose-built for AI agents, automations, and decisioning engines. You’ll also apply data science and model-tuning techniques to optimize LLMs and solve complex marketing challenges for our clients.
If you enjoy turning high-volume data into clean, powerful systems—and you want your work to drive real business outcomes rather than just dashboards—this role puts you right at the center of the action.
What You’ll Do
- Apply bleeding edge AI theory to the design and implementation of large-scale data systems that feed AI agents and autonomous workflows.
- Use data science techniques to fine-tune, evaluate, and optimize LLMs for marketing-specific tasks: attribution insights, anomaly detection, summarization, classification, and automated recommendations.
- Build end-to-end automations using LLMs, internal data, and external signals to eliminate repetitive human tasks.
- Build AI-driven automations that reduce manual work across Rockerbox and unlock new client-facing capabilities.
- Design retrieval, orchestration, and memory layers that make our AI agents smarter over time.
- Establish best practices for AI data quality, observability, experiments, and safety.
- Lead R&D initiatives: rapid prototyping, experimentation, model evaluations, and productionization.
- Mentor data scientists and engineers across the organization to raise the bar on LLM use company-wide.
What You Bring
- 8+ years of experience in data engineering, AI, ML platforms, or large-scale distributed systems.
- Hands-on experience integrating LLMs into production systems (OpenAI, fine-tuning, embeddings, RAG, vector stores, or custom agent orchestration).
- Strong understanding of experimentation, model evaluation, and performance tuning.
- You think in systems: storage, retrieval, metadata, reliability, latency, failure modes.
- Ability to work from ambiguity to execution — you’re comfortable being the first to figure something out.
- Strong communication skills: you can explain tradeoffs, scope decisions, and technical strategy clearly.
Staff AI Data Engineer - Rockerbox employer: DoubleVerify
DoubleVerify is an exceptional employer that champions diversity and inclusion, offering a dynamic work culture where innovation thrives. Employees benefit from comprehensive growth opportunities, competitive compensation, and a supportive environment that values collaboration across teams. With a commitment to transparency and excellence in digital advertising, working at DoubleVerify means being part of a mission-driven company that empowers its employees to make a meaningful impact in the industry.
StudySmarter Expert Advice🤫
We think this is how you could land Staff AI Data Engineer - Rockerbox
✨Get Involved in Data Science Meetups
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We think you need these skills to ace Staff AI Data Engineer - Rockerbox
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at DoubleVerify, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at DoubleVerify. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at DoubleVerify
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at DoubleVerify!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
