What we’re looking for

This is a highly visible, strategic, and impactful role, reporting the VP of Engineering for SurveyMonkey Core Engineering. As the Senior Director of AI, you will be a critical leader in defining the future of SurveyMonkey's products and services. You will not just manage the Machine Learning and Data Science teams, but help drive the fundamental shift in how our customers gather, analyze, and act on feedback. This requires both strong strategic thinking, and solid experience building models and deploying AI/ML systems end-to-end (from data to production). The successful candidate will make a material contribution to the company's success by providing thought leadership around how to leverage AI technology in the SurveyMonkey product in an intuitive and natural way.

What you’ll be working on

Strategic Leadership & Product Alignment

• Strategic Roadmapping: Define and execute the long-term data science strategy, ensuring alignment with SurveyMonkey’s overarching business objectives and product vision.
• Product-to-Data Science Translation: Act as the primary liaison between Product Management and the Data Science team, expertly mapping ambitious SurveyMonkey product requirements (e.g., automated thematic analysis, predictive modeling, intelligent survey generation) into clear, technically feasible data science projects.
• AI-First Transformation: Support the charge in migrating SurveyMonkey from a traditional SaaS product to an AI-first platform, embedding advanced data science and LLM capabilities deeply within the product experience.

Technical Expertise & Innovation

• LLM and NLP Integration: Drive the strategy for integrating and leveraging Large Language Models (LLMs) and advanced Natural Language Processing (NLP) techniques to enhance our text analytics, sentiment analysis, and generative features within the product.
• Familiarity with MLOps / LLMOps practices: Experience developing and deploying a Machine Learning Platform.  Experience with Agentic AI Architecture, deployment and execution. Familiarity with modern MLOps tools and platforms (e.g., AWS SageMaker, GCP Vertex AI, MLflow).
• Technical Direction: Set the technical standards for model development, deployment, monitoring, and maintenance. Ensure the team utilizes best-in-class data science models, tools, and platforms for scalable production systems.
• Ownership of model lifecycle: experimentation → deployment → monitoring → iteration
• Stay Current: Actively research, evaluate, and evangelize the latest developments in machine learning, deep learning, MLOps, and data science methodologies, ensuring the team's capabilities remain cutting-edge.

Team Management & Development

• Management and Motivation: Lead, mentor, and inspire a high-performing team of data scientists and machine learning engineers. Foster a culture of innovation, rigor, collaboration, and continuous learning.
• Recruitment and Growth: Recruit top-tier data science talent, manage performance, and define career progression paths to ensure the ongoing growth and retention of team members.

We’d love to hear from people with

• Experience: 10+ years of progressive experience in data science, including significant experience with AI/ML platforms, and at least 5 years in a Director level leadership role managing and scaling data science or machine learning teams in a commercial software environment.
• Experience building or leading AI/ML platforms: (feature stores, model serving, training pipelines) in a Cloud Environment like AWS
• Deep Technical Acumen: Extensive practical experience with and deep theoretical understanding of machine learning algorithms, statistical modeling, and experimental design (A/B testing).
• NLP and LLM Proficiency (Critical): Demonstrated experience in applying, fine-tuning, and integrating Large Language Models (LLMs) and advanced Natural Language Processing techniques into production systems, especially for tasks related to text classification, summarization, and generation.
• Product Thinking: Exceptional ability to translate ambiguous or high-level product needs into concrete, defensible data science problems and solutions.
• Communication: Excellent written and verbal communication skills, with the ability to articulate complex technical concepts to non-technical stakeholders (e.g., Executives, Product Managers, Marketing).
• Education: Master’s degree or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field.

The base pay provided for this position ranges from CAD $224,000/ year - $336,000/ year depending on the geographic market and assuming a full-time schedule. Actual base pay is based on a number of factors including market location, job-related knowledge, education or training, skills, and experience.

Bonuses may also be offered as part of the total compensation package, in addition to a competitive benefits package including medical, dental, vision, life, and disability insurance; RRSP matching; flexible spending & health savings account; paid holidays; paid time off; employee assistance program; and other company benefits. 

This opening is for an existing vacancy.

SurveyMonkey believes in-person collaboration is valuable for building relationships, fostering community, and enhancing our speed and execution. While this role is remote, it may require in-person participation. You will be encouraged to attend company events throughout the year. These events will take place at a designated SurveyMonkey office or location.

#LI-remote

What we’re looking for

This is a highly visible, strategic, and impactful role, reporting the VP of Engineering for SurveyMonkey Core Engineering. As the Senior Director of AI, you will be a critical leader in defining the future of SurveyMonkey's products and services. You will not just manage the Machine Learning and Data Science teams, but help drive the fundamental shift in how our customers gather, analyze, and act on feedback. This requires both strong strategic thinking, and solid experience building models and deploying AI/ML systems end-to-end (from data to production). The successful candidate will make a material contribution to the company's success by providing thought leadership around how to leverage AI technology in the SurveyMonkey product in an intuitive and natural way.

What you’ll be working on

Strategic Leadership & Product Alignment

• Strategic Roadmapping: Define and execute the long-term data science strategy, ensuring alignment with SurveyMonkey’s overarching business objectives and product vision.
• Product-to-Data Science Translation: Act as the primary liaison between Product Management and the Data Science team, expertly mapping ambitious SurveyMonkey product requirements (e.g., automated thematic analysis, predictive modeling, intelligent survey generation) into clear, technically feasible data science projects.
• AI-First Transformation: Support the charge in migrating SurveyMonkey from a traditional SaaS product to an AI-first platform, embedding advanced data science and LLM capabilities deeply within the product experience.

Technical Expertise & Innovation

• LLM and NLP Integration: Drive the strategy for integrating and leveraging Large Language Models (LLMs) and advanced Natural Language Processing (NLP) techniques to enhance our text analytics, sentiment analysis, and generative features within the product.
• Familiarity with MLOps / LLMOps practices: Experience developing and deploying a Machine Learning Platform.  Experience with Agentic AI Architecture, deployment and execution. Familiarity with modern MLOps tools and platforms (e.g., AWS SageMaker, GCP Vertex AI, MLflow).
• Technical Direction: Set the technical standards for model development, deployment, monitoring, and maintenance. Ensure the team utilizes best-in-class data science models, tools, and platforms for scalable production systems.
• Ownership of model lifecycle: experimentation → deployment → monitoring → iteration
• Stay Current: Actively research, evaluate, and evangelize the latest developments in machine learning, deep learning, MLOps, and data science methodologies, ensuring the team's capabilities remain cutting-edge.

Team Management & Development

• Management and Motivation: Lead, mentor, and inspire a high-performing team of data scientists and machine learning engineers. Foster a culture of innovation, rigor, collaboration, and continuous learning.
• Recruitment and Growth: Recruit top-tier data science talent, manage performance, and define career progression paths to ensure the ongoing growth and retention of team members.

We’d love to hear from people with

• Experience: 10+ years of progressive experience in data science, including significant experience with AI/ML platforms, and at least 5 years in a Director level leadership role managing and scaling data science or machine learning teams in a commercial software environment.
• Experience building or leading AI/ML platforms: (feature stores, model serving, training pipelines) in a Cloud Environment like AWS
• Deep Technical Acumen: Extensive practical experience with and deep theoretical understanding of machine learning algorithms, statistical modeling, and experimental design (A/B testing).
• NLP and LLM Proficiency (Critical): Demonstrated experience in applying, fine-tuning, and integrating Large Language Models (LLMs) and advanced Natural Language Processing techniques into production systems, especially for tasks related to text classification, summarization, and generation.
• Product Thinking: Exceptional ability to translate ambiguous or high-level product needs into concrete, defensible data science problems and solutions.
• Communication: Excellent written and verbal communication skills, with the ability to articulate complex technical concepts to non-technical stakeholders (e.g., Executives, Product Managers, Marketing).
• Education: Master’s degree or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field.

The base pay provided for this position ranges from CAD $224,000/ year - $336,000/ year depending on the geographic market and assuming a full-time schedule. Actual base pay is based on a number of factors including market location, job-related knowledge, education or training, skills, and experience.

Bonuses may also be offered as part of the total compensation package, in addition to a competitive benefits package including medical, dental, vision, life, and disability insurance; RRSP matching; flexible spending & health savings account; paid holidays; paid time off; employee assistance program; and other company benefits. 

This opening is for an existing vacancy.

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 our Ottawa office up to 1 day per week.

#LI-Hybrid

About the Role

As a Context Engineer at CapIntel, you'll sit at the intersection of AI infrastructure and engineering. You will be responsible for how large language models are integrated into our core platform and how our engineering team adopts agentic workflows. This is a hands-on, production-focused role, not a research one. You'll build the systems that make our AI features reliable, accurate, and scalable for the wealth management enterprises that depend on us.

You'll be embedded in development teams working closely with engineers, product managers, and domain experts across the organization to design and deliver LLM-powered capabilities that directly enhance the advisor and client experience. As one of the first practitioners in this discipline at CapIntel, you'll also help define what context engineering looks like here: setting patterns and practices the broader team can build on.

This role is ideal for someone who thinks in systems, cares about production reliability over demo-day performance, and is energized by working in a discipline that is evolving quickly.

What You'll Do

• Design and implement LLM-powered features into our core application via model APIs (e.g. Anthropic, OpenAI, Cohere), with a focus on reliability and production-readiness
• Architect and maintain retrieval-augmented generation (RAG) pipelines, connecting language models to internal knowledge bases, databases, and live data sources
• Manage context window strategy, determining what information enters the model, when, in what format, and at what level of compression to optimise for accuracy, cost, and latency
• Design and implement agentic workflows enabling the platform to handle multi-step, autonomous tasks
• Build guardrail and output validation layers that constrain model behaviour and ensure AI features act within well-defined, compliant boundaries
• Develop reusable agent primitives, prompt templates, and workflow components that other engineers can build on independently
• Build evaluation frameworks to measure context effectiveness, output quality, and agent reliability in production
• Monitor deployed AI systems for failure patterns and implement mitigation strategies, feeding learnings back into continuous improvement cycles
• Collaborate with Product, Product Engineering, Implementation, and Data teams to translate business requirements, and proof of concepts into production AI system specifications
• Act as an internal practitioner and resource helping upskill the broader engineering team on context engineering principles and agentic best practices

What We're Looking For

• 5+ years of professional software engineering experience, with at least 1–2 years working with LLMs in a production context
• Strong experience with Python or Node and building API-integrated backend services
• Hands-on experience with an orchestration or execution framework
• Working knowledge of RAG architecture, vector databases (e.g. Pinecone, pgVector, AWS OpenSearch), and semantic search
• Familiarity with context management techniques: summarisation, chunking, session splitting, and memory strategies
• Experience building or consuming REST APIs and integrating with third-party services
• Comfortable collaborating with cross-functional teams in a fast-paced, high-growth environment
• Strong problem-solving instincts and a willingness to learn and adapt as the field evolves

Nice to Have

• Experience with the Model Context Protocol (MCP) or similar tool-integration standards
• Familiarity with LLMOps practices: tracing, observability (e.g. LangSmith, Datadog), and model versioning
• Exposure to multi-agent architectures and orchestration patterns
• Knowledge of AI output validation, context safety, and governance considerations particularly relevant in regulated industries like financial services
• Familiarity with AWS or cloud-based infrastructure and containerised deployments (Docker, Kubernetes)
• Ability to communicate technical concepts clearly to both technical and non-technical partners