Staff Machine Learning Engineer
Pubblicato il 17-08-2026 - Iniziative benefiche e non lucrative in Roma
SurveyMonkey is the world's most popular platform for surveys and forms, built for business—loved by users. Si assicuri di leggere l'intera descrizione riportata di seguito e di candidarsi immediatamente se è affidabile/a di soddisfare tutti i requisiti. We combine powerful capabilities with intuitive design, effectively serving every use case, from customer experience to employee engagement, market research to payment and registration forms. With built-in research expertise and AI-powered technology, it's like having a team of expert researchers at your fingertips. Trusted by millions—from startups to Fortune 500 companies—SurveyMonkey helps teams gather insights and information that inspire better decisions, create experiences people love, and drive business growth. Discover how at . What we're looking for: We're seeking a Staff Machine Learning Engineer to join the Product Data Science team, a key contributor to our AI/ML growth. As a technical leader, you'll join a team building enterprise-scale NLP, ML and AI systems that power the product across the organisation.
This role is an opportunity to architect ML/AI solutions from the ground up: crafting reusable and impactful components, defining architectures and models for complex tasks, and serving as a technical expert and mentor who shapes both our technology stack and team capabilities. You'll have the autonomy to define end-to-end solutions for ambiguous, high-impact problems while contributing to strategic direction and building the next generation of ML talent at the company. What you'll be working on: Serve as a technical leader for the ML/AI product initiatives. Authoring, approving, and guiding architectural decisions. Establishing standards and frameworks that can be adopted across ML initiatives. Handle deliverables, delegate effectively, and mentor engineers across teams. Architected scalable ML platforms and production data pipelines, collaborating cross-functionally to integrate new data sources. Build