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SurveyMonkey

Machine Learning Engineer - Multiple Positions

Ottawa, ON, Canada

Job type: Full-time
Apply by: Oct 28, 2026
Posting language: English
Posted: Sep 28, 2026

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Original job description

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.

We’d Love To Hear From People With

Extensive professional 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 per week.

- 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.

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More about this job

Responsibilities

Build, deploy, monitor, and iteratively improve end-to-end machine learning and AI solutions, including scalable platforms, production data pipelines, and product integrations using statistical methods and large language models. Collaborate with Product, Design, Data Science, and engineering teams to develop evaluations and feedback loops, guide technical strategy, and mentor engineers.

Requirements

Candidates should have extensive professional experience building and maintaining production machine learning and data science systems, with strong expertise in NLP, statistical modeling, model evaluation, and production monitoring. The role also calls for experience with SaaS and AWS deployments, LLM-powered integrations such as agents and RAG, and technical leadership and mentorship.

Skills

Machine Learning
Natural Language Processing
Statistical Modeling
Large Language Models
Model Evaluation
Production Monitoring
Machine Learning Platforms
Data Pipelines
Human-in-the-Loop Labeling
Active Learning
AWS
Kafka
Amazon EKS
Amazon SageMaker
Amazon Athena
Retrieval-Augmented Generation

Visa sponsorship

Not detected in the job text

Keywords

Machine Learning
Artificial Intelligence
Natural Language Processing
Statistical Modeling
Large Language Models
Generative AI
Model Fine-Tuning
Model Deployment
Model Evaluation
LLM-as-Judge
Production Monitoring
Telemetry
Machine Learning Platforms
Production Data Pipelines
Human-in-the-Loop Labeling
Exploratory Data Analysis
Active Feedback
SaaS
CI/CD
AWS
Kafka
Amazon EKS
Amazon SageMaker
Amazon Athena
AI Agents
Autonomous Processes
Retrieval-Augmented Generation
Context Enrichment
Prompt Engineering
Guided Generation

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