Staff Machine Learning Engineer

What we’re looking for

At SurveyMonkey, we harness AI and Machine Learning to delight customers at every stage of their journey. The Machine Learning Platform (MLP) team owns the complete ML operations pipeline. You will be a driving force for a team that builds and enhances a platform that accelerates machine learning adoption across SurveyMonkey's product portfolio. You will report to the Director of Machine Learning. 

What you’ll be working on

As a Staff ML Engineer, you will:

• Design and implement secure, scalable, and high-performance pipelines managing the end-to-end lifecycle of ML models. 
• Offer strong technical leadership skills, have served as a consultant to management and internal/external spokesperson for the ML team, and educate and influence leadership on decisions affecting ML. 
• You'll integrate, test, and monitor ML model services across our product portfolio. The MLP team's focus includes supporting services for ML models and extending architecture to integrate with other SurveyMonkey microservices.
• Collaborate closely with SurveyMonkey's application engineers 
• Use applied machine learning techniques that include generative AI, natural language processing, classification, spam detection, personalization/ranking, etc. We do this at a significant scale and generally in real-time.
• Being fun to work with is definitely a plus!

We’d love to hear from people with

• Build and maintain ML systems using Python (including Pandas, NumPy, PySpark, etc.) for efficient ML operations.
• 10+ years of MLOps experience in designing and implementing ML cloud infrastructure using AWS services.
• Lead requirements collection, negotiate architectural decisions, and ensure platform scalability.
• Deep understanding of machine learning algorithms including large language models, applying them effectively in ML projects.
• Design machine learning platforms for reuse and scalability, incorporating telemetry for complex failure mode analysis.
• Mastery of machine learning concepts like supervised and unsupervised learning, driving innovative solutions.
• Comfortable with Unix/Linux systems, strong communication, and documentation skills.
• Ability to deal well with ambiguity and deliver great results to meet changing needs.

Nice to have

• Experience with operating computational clusters for training ML models.
• Proven track record in deploying end-to-end solutions with creative problem-solving skills to handle a high volume of throughput. 

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