Staff AI Engineer

About the role

As a Staff AI Engineer, you'll serve as a technical leader for our LLM-powered products at the forefront of marketing and advertising technologies. You'll own critical architectural decisions, set quality bars, and lead multi‑team initiatives that drive measurable outcomes. As our Staff AI Engineer, you will lead the vision and execution of our data platform and LLM-powered products. You'll own critical architectural decisions across data pipelines, model integration, model deployment and evals: turning high-level product and business requirements into robust, scalable data products that drive measurable outcomes for our Fortune 500 customers. This role spans data backend and ML engineering: from the reliability and cost efficiency of our pipelines to the outcome and performance of LLM-enabled features. You'll raise the bar for data literacy across the department, craft and collaboration. You'll be accountable for the health, cost, and evolution of key data product and data platforms, partnering closely with full-stack engineers, product, design, and devops to deliver outcomes our customers can trust. This role presents an exciting opportunity to shape the future of AI-driven technologies and make meaningful contributions to real-world applications. The role is linked with our location in London, but we are flexible about hybrid or remote work in The United Kingdom.

Responsibilities

• Lead end-to-end architecture for data platforms and pipelines: scraping, data extraction, transformation, storage, serving, and ML/LLM integration, balancing performance, reliability, security, and cost.
• Incrementally scale pipelines and systems: design safe rollout plans and north star data-quality metrics to handle customer and traffic growth without impacting production.
• Translate business goals into actionable data products: assess high-level requirements, carve clear problem spaces, draft crisp RFCs, and sequence work into deliverable projects for the team.
• Establish and enforce engineering standards: testing strategy, evals, observability, data contracts, and security practices across services. Think through short-term and long-term goals to come up with fast go-to-market products, while planning ahead for productization.
• Up‑level the org: lead architecture reviews, codify patterns, mentor Senior Engineers, and multiply impact through documentation, code reviews, and pairing.
• Startup‑ready: flexible, comfortable with ambiguity and constant change; proactive about process, documentation, and reliability without over‑engineering.
• Ship meaningful experiments: prototype data/ML capabilities, evaluate feasibility and ROI, and make pragmatic calls on productionalizing with an eye on operating costs and risk.

Requirements

• 8+ years building and operating production data systems, including leading cross-cutting architectural changes, and deploying LLMs in real‑world scenarios at scale.
• Deep experience Python and modern service architectures; strong system design and data modeling fundamentals.
• Extensive experience with training and deploying machine learning models, particularly within the NLP/LLM domain. Proficiency in Python. Familiarity with infrastructure as code, CI/CD, and cloud infrastructure.
• Fluency in operational maturity: SLOs, on‑call/incident practices, and observability.
• Strong analytical and problem-solving abilities, with a bias towards action and outcomes. Experience with data preprocessing, feature engineering, and model evaluation techniques.
• Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders. Demonstrated leadership experience, with the ability to guide and inspire a team.
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