Machine Learning Engineer

SurveyMonkey is the world’s most popular platform for surveys and forms, built for business—loved by users. 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 surveymonkey.com.

What we’re looking for:

SurveyMonkey's Machine Learning organization spans two connected tracks: the Machine Learning Platform (MLP) team, which builds the secure, scalable pipelines and infrastructure that deploy and monitor ML models in production, and the Product Data Science team, which designs, builds, and fine-tunes the models, from statistical methods to LLMs, before they reach deployment. We're building enterprise-scale NLP and ML systems, and looking for talented ML engineers at different levels, from early career through to Staff Engineer. Whether you're a technical leader ready to shape our ML architecture and mentor teams, or an engineer looking to own end-to-end ML solutions, we have roles that could fit.

You'll work at the intersection of Data Science, DevOps, and Product, helping power technologies like Generative AI, NLP, and real-time classification across SurveyMonkey's product portfolio.

What you’ll be working on:

 

• Build and own end-to-end ML/AI solutions within the product, maintaining long-term ownership through different stages of deployment, refinement, and iterative enhancements

• Build end-to-end monitoring and telemetry systems to detect complex failure modes, deterioration of predictive accuracy, and usage patterns.

• Architect scalable ML platforms and production data pipelines, collaborating cross-functionally to integrate new data sources.

• Create novel and traditional ML/AI implementations for the in-product experience, collaborating with Product, Design, Front- and Back-End developers to educate teams and iterate through solutions and designs.

• Deliver tailored AI solutions by training, fine-tuning, and deploying models ranging from statistical methods to cutting-edge LLMs.

• Drive continuous improvement and innovation through research, proposing adoption strategies for external solutions, identifying knowledge gaps within the team, and ensuring hiring and training initiatives address capability needs.