Asks for

The ad says“SaaS development and deployment experience, building multi-scaled, right-sized ML solutions in SaaS environments with CI/CD code lifecycle practices in AWS environments and services (Kafka, EKS, SageMaker, Athena).”
The ad says“SaaS development and deployment experience, building multi-scaled, right-sized ML solutions in SaaS environments with CI/CD code lifecycle practices in AWS environments and services (Kafka, EKS, SageMaker, Athena).”
The ad says“SaaS development and deployment experience, building multi-scaled, right-sized ML solutions in SaaS environments with CI/CD code lifecycle practices in AWS environments and services (Kafka, EKS, SageMaker, Athena).”
The ad says“Experience handling data at scale with familiarity in Human-In-The-Loop labelling, training and scaling usable data, plus foundational exploratory data analysis.”
The ad says“Experience handling data at scale with familiarity in Human-In-The-Loop labelling, training and scaling usable data, plus foundational exploratory data analysis.”
The ad says“SaaS development and deployment experience, building multi-scaled, right-sized ML solutions in SaaS environments with CI/CD code lifecycle practices in AWS environments and services (Kafka, EKS, SageMaker, Athena).”
The ad says“Proven expertise in creating evaluations and evaluation platforms for non-deterministic systems, including LLM-as-Judge techniques, inferred signals, and traditional model evaluation mechanisms to validate performance and maximise ML/AI impact.”
The ad says“Strong expertise in Natural Language Processing, statistical modelling and analysis, and modern ML methods. Deep understanding of not just the models, but what's happening within them—hands-on experience building novel architectures for bespoke product solutions.”
The ad says“Demonstrated experience building LLM-powered product integrations, including Agents and autonomous processes, RAG and context enrichment, and modern prompt engineering methods with guided generation and oversight.”
The ad says“Demonstrated experience building LLM-powered product integrations, including Agents and autonomous processes, RAG and context enrichment, and modern prompt engineering methods with guided generation and oversight.”
The ad says“SaaS development and deployment experience, building multi-scaled, right-sized ML solutions in SaaS environments with CI/CD code lifecycle practices in AWS environments and services (Kafka, EKS, SageMaker, Athena).”
The ad says“SaaS development and deployment experience, building multi-scaled, right-sized ML solutions in SaaS environments with CI/CD code lifecycle practices in AWS environments and services (Kafka, EKS, SageMaker, Athena).”
The ad says“Strong expertise in Natural Language Processing, statistical modelling and analysis, and modern ML methods. Deep understanding of not just the models, but what's happening within them—hands-on experience building novel architectures for bespoke product solutions.”
The ad says“Strong expertise in Natural Language Processing, statistical modelling and analysis, and modern ML methods. Deep understanding of not just the models, but what's happening within them—hands-on experience building novel architectures for bespoke product solutions.”

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:

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 and own end-to-end ML/AI solutions within the product, maintaining long-term ownership through different stages of deployment, refinement, and iterative enhancements
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.
Build end-to-end monitoring and telemetry systems to detect complex failure modes, deterioration of predictive accuracy, and usage patterns.
Deliver tailored AI solutions by training, fine-tuning, and deploying models ranging from statistical methods to cutting-edge LLMs.
Build productive partnerships across all stakeholder levels, from non-technical to domain experts, with an open, inclusive, and expert outlook.
Drive continuous improvement and innovation through research, proposing adoption strategies of external solutions, identifying knowledge gaps within the team, and ensuring hiring and training initiatives address capability needs.

We’d love to hear from people with

10+ years of experience in Machine Learning and Data Science, building and maintaining models in production environments.

Strong expertise in Natural Language Processing, statistical modelling and analysis, and modern ML methods. Deep understanding of not just the models, but what's happening within them—hands-on experience building novel architectures for bespoke product solutions.
Experience handling data at scale with familiarity in Human-In-The-Loop labelling, training and scaling usable data, plus foundational exploratory data analysis.
Proven expertise in creating evaluations and evaluation platforms for non-deterministic systems, including LLM-as-Judge techniques, inferred signals, and traditional model evaluation mechanisms to validate performance and maximise ML/AI impact.
Experience developing production monitoring and creating feedback loops using active feedback, passive signals, and user behaviours to identify key performance indicators for user journeys and experiences.
SaaS development and deployment experience, building multi-scaled, right-sized ML solutions in SaaS environments with CI/CD code lifecycle practices in AWS environments and services (Kafka, EKS, SageMaker, Athena).
Demonstrated experience building LLM-powered product integrations, including Agents and autonomous processes, RAG and context enrichment, and modern prompt engineering methods with guided generation and oversight.
Proven leadership and mentorship capabilities as a technical leader with the ability to mentor engineers across teams and manage complex deliverables.

SurveyMonkey believes in-person collaboration is valuable for building relationships, fostering community, and enhancing our speed and execution in problem-solving and decision-making. As such, you will be required to work from a SurveyMonkey office for up to 1 day every two weeks.

#LI - Hybrid

Why SurveyMonkey? We’re glad you asked

At SurveyMonkey, curiosity powers everything we do. We’re a global company where people from all backgrounds can make an impact, build meaningful connections, and grow their careers. Our teams work in a flexible, hybrid environment with thoughtfully designed offices and programs like the CHOICE Fund to help employees thrive in work and life.

We’ve been trusted by organizations for over 25 years, and we’re just getting started. Our milestones include celebrating a quarter-century of curiosity with 25 acts of giving, opening new hubs in Costa Rica and India, crossing the threshold of 100 billion questions answered, and earning recognition as one of the Most Inspiring Workplaces across North America and Asia.

We live our company values—like championing inclusion and making it happen—by embedding them into how we hire, collaborate, and grow. They help shape everything from our culture to our business decisions. Come join us and see where your curiosity can take you.

Our commitment to an inclusive workplace

SurveyMonkey is an equal opportunity employer committed to providing a workplace free from harassment and discrimination. We celebrate the unique differences of our employees because that is what drives curiosity, innovation, and the success of our business. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, gender identity or expression, age, marital status, veteran status, disability status, pregnancy, parental status, genetic information, political affiliation, or any other status protected by the laws or regulations in the locations where we operate. Accommodations are available for applicants with disabilities.

Your data

For more information on how SurveyMonkey (including its subsidiary and affiliated companies) processes your personal data as a job candidate or applicant, please see our Global Applicant and Candidate Data Privacy Notice. Please note that we may use artificial intelligence (AI) tools to support parts of the hiring process, such as sourcing candidates, reviewing applications, analyzing resumes, or summarizing interviews. These tools assist our recruitment team but do not replace human judgment.