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- 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.
- 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.
- Other info: Collaborative environment with mentorship opportunities and cutting-edge projects.
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.
- 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
Contact Detail:
DoubleVerify Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Staff AI Data Engineer - Rockerbox
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving AI and data engineering. This gives you a chance to demonstrate your expertise beyond just a CV.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and understanding the latest trends in AI and data systems. Practice explaining complex concepts in simple terms—this will impress interviewers and show your communication prowess.
✨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, it shows you’re genuinely interested in joining Rockerbox and being part of our exciting journey.
We think you need these skills to ace Staff AI Data Engineer - Rockerbox
Some tips for your application 🫡
Show Your Passion for AI: When writing your application, let your enthusiasm for AI and data engineering shine through. We want to see how you can apply cutting-edge AI theories in real-world scenarios, so share any relevant projects or experiences that highlight your passion.
Tailor Your Experience: Make sure to customise your application to reflect the specific skills and experiences mentioned in the job description. Highlight your hands-on experience with LLMs and data systems, and don’t forget to mention any mentoring roles you've taken on – we love a team player!
Be Clear and Concise: We appreciate clarity! Use straightforward language to explain your technical skills and experiences. Avoid jargon where possible, and focus on how your background aligns with our mission at Rockerbox. Remember, we want to understand your thought process and problem-solving abilities.
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it shows you’re serious about joining our team at Rockerbox!
How to prepare for a job interview at DoubleVerify
✨Know Your AI Stuff
Make sure you brush up on the latest AI theories and practices, especially those related to LLMs. Be ready to discuss how you've applied these concepts in real-world scenarios, particularly in data engineering and automation.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've tackled complex marketing challenges using data science techniques. Highlight your experience with model tuning and performance evaluation, as this will demonstrate your ability to drive real business outcomes.
✨Communicate Clearly
Practice explaining technical concepts in a way that's easy to understand. You’ll need to convey trade-offs and decisions clearly, so think about how you can break down complex ideas into digestible bits for your interviewers.
✨Be Ready to Lead
Since mentoring is part of the role, come prepared with examples of how you've guided others in your field. Discuss any R&D initiatives you've led and how you’ve raised the bar for AI practices in your previous roles.
