Staff ML Engineer - NLP & AI Platform Lead

Tree Trust; Italia; Hybrid; EUR 122,570 - 157,590; Full time

Benefits offered by this job

Hybrid work model

Office on-site days every two weeks

Job summary

SurveyMonkey is seeking a Staff Machine Learning Engineer to join the Product Data Science team. You will architect enterprise-scale NLP, ML, and AI systems powering the product, mentor engineers, and own end-to-end ML solutions from data ingestion to deployment.

You'll collaborate across Product, Design, Front- and Back-End teams to deliver scalable, reusable components and drive the next generation of ML talent within a global, hybrid work environment.

Qualifications

• 10+ years of experience in ML/DS building and maintaining models in production environments.
• Strong expertise in NLP, statistical modelling and modern ML methods.
• Experience handling data at scale with HITL labelling and exploratory data analysis.
• Proven expertise in creating evaluations and evaluation platforms for non-deterministic systems, including LLM‑as‑Judge techniques.
• Experience developing production monitoring and feedback loops using signals and user behaviours.

Responsibilities

• Serve as a technical leader for ML/AI product initiatives and set architectural standards.
• Mentor engineers, delegate effectively, and manage multi-team deliverables.
• Architect scalable ML platforms and production data pipelines; integrate new data sources.
• Build end-to-end ML/AI solutions within the product and own them through deployment and iteration.
• Collaborate with Product, Design, Front- and Back-End teams to educate and iterate on solutions.

Skills

ML & DS leadership

NLP & modeling

Production ML systems

Data at scale

MLOps & AWS

Tools

Kafka

EKS

SageMaker

Athena

Job description

SurveyMonkey is seeking a Staff Machine Learning Engineer to join the Product Data Science team. You will architect enterprise-scale NLP, ML, and AI systems powering the product, mentor engineers, and own end-to-end ML solutions from data ingestion to deployment.

You'll collaborate across Product, Design, Front- and Back-End teams to deliver scalable, reusable components and drive the next generation of ML talent within a global, hybrid work environment.