Staff AI Data Engineer
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Requirements
• 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. • The successful candidate’s starting salary will be determined based on a number of non-discriminating factors, including qualifications for the role, level, skills, experience, location, and balancing internal equity relative to peers at DV.The estimated salary range for this role based on the qualifications set forth in the job description is between [$128,000 - $230,000]. This role will also be eligible for bonus/commission (as applicable), equity, and benefits.The range above is for the expectations as laid out in the job description; however, we are often open to a wide variety of profiles, and recognize that the person we hire may be more or less experienced than this job description as posted.Why You’ll Love Rockerbox: At Rockerbox, you’ll find a fast-paced, results-driven environment where your work has a direct impact on our growth and the success of our clients. Our iterative development process means you’ll see your contributions come to life quickly. You’ll join a supportive, light-hearted team that values collaboration and innovation. We are committed to professional growth and will actively support your development in both technical and business domains. • Not-so-fun fact: Research shows that while men apply to jobs when they meet an average of 60% of job criteria, women and other marginalized groups tend to only apply when they check every box. So if you think you have what it takes but you’re not sure that you check every box, apply anyway! • Not-so-fun fact:
Responsibilities
• 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 organization to raise the bar on LLM use company-wide.