AI/Machine Learning Engineer

Quandri Technologies, Inc.; Vancouver; Hybrid; CAD 100,000 - 125,000; Full time

Job summary

Join a forward-thinking company revolutionizing the insurance industry with AI-driven automation. As a Senior ML/AI Engineer, you will design and implement cutting-edge machine learning solutions that enhance operational efficiency and client satisfaction. This role offers the chance to work with a dynamic team, leveraging modern technologies to transform the renewal process for brokerages. With a focus on collaboration and innovation, you will play a pivotal role in shaping the future of insurance. If you're passionate about AI and eager to make a significant impact, this is the perfect opportunity for you.

Benefits

Employee stock options

Comprehensive health benefits

Four weeks of paid vacation

Work remotely for 60 days

Lifestyle Spending Account

Qualifications

• Proven experience in building scalable ML pipelines and AI-driven solutions.
• Hands-on experience with modern ML frameworks and cloud services.
• Strong analytical problem-solving skills and ability to mentor others.

Responsibilities

• Design and deploy machine learning models for automation and efficiency.
• Build and maintain scalable ML pipelines for data ingestion and model training.
• Collaborate with cross-functional teams to integrate AI capabilities.

Skills

Machine Learning

Data Engineering

Python

SQL

Deep Learning

AI Automation

Model Optimization

Data Analysis

Cloud Services

CI/CD for ML

Education

Bachelor's Degree in Computer Science

Master's Degree in AI or Machine Learning

Tools

TensorFlow

PyTorch

Apache Spark

MLflow

Kubeflow

SageMaker

Docker

Kubernetes

Apache Kafka

Power BI

Job description

We’re Quandri, our mission is to unlock the world’s insurance data so brokerages and agencies can best serve their clients. Our Renewal Intelligence Platform is designed to help brokerages save time, increase profitability, and drive better outcomes for their staff, clients, and business.

We saw 3x ARR growth last year and have plans to continue to grow both revenue and our team this year. Named one of LinkedIn’s Top Canadian Startups in 2024, we have already made a big impact on the insurance industry. However, what matters most is making our customer’s lives better one renewal at a time. We want you to be a critical part of that journey! We’re a hybrid company, with ⅔ of our team in Vancouver and the rest distributed. For those in Vancouver, we have an office in Gastown that we expect people to be at three days a week. We understand both the advantages of some flexibility around personal lives, and the positive interpersonal effects of in-person collaboration.

Running a profitable personal lines book of business is harder than ever for insurance brokerages. Market conditions, rising costs, talent shortages, and staffing constraints are just some of the challenges that hinder profit margins, scalability, and exceptional client service. Trusted by 5 of Canada’s top 10 brokerages, Quandri is transforming the renewal process with AI-driven automation, enabling proactive workflows and delivering data-driven insights.

Today’s renewal process is often reactive, with brokers focusing on clients who request help rather than adopting a proactive, data-driven approach. Quandri is revolutionizing renewals by offering a platform that uses AI and automation to streamline operations. This allows brokerages to retain more business, enhance client and staff experiences, reduce E&O risk, and boost sales through upselling and cross-selling.

Are you looking to make an impact in your next role? How about transforming an entire industry? At Quandri, we’re unlocking new frontiers in insurance. To do that, we model our culture as a crew of interstellar astronauts. As Quandronauts, we’re committed to building a company that is diverse and multi-faceted. We’ve raised venture capital from top US and Canadian investors to help us achieve our mission, and are now scaling to achieve this.

About the Role:

At Quandri, we're embarking on an exciting journey to transform the workforce through automation, powered by AI and robotics. We're looking for a ML/AI Engineer to join our team. This role presents a unique opportunity to design and build cutting-edge machine learning and AI-driven solutions from the ground up. You will play a crucial role in shaping our AI infrastructure, optimizing models for real-world applications, and ensuring the scalability and efficiency of our AI-powered automation systems.

As a Senior ML/AI Engineer, you will be responsible for developing and deploying ML models, improving inference pipelines, and integrating AI capabilities into our automation platform. Your work will span from researching state-of-the-art techniques to implementing scalable AI solutions in production. You will break down complex AI-driven projects, understand the interplay between various ML components, and enhance the technical robustness of our AI ecosystem.

In this role, you will leverage your expertise in machine learning, deep learning, data engineering, and software development to build and optimize intelligent systems. You will work closely with our Chief Architect, Data Engineers, Software Developers, Product Owners, and DevOps Engineers to drive AI innovations. A team of experts will support you, empowering you to confidently navigate challenges and bring impactful solutions to life.

We are seeking more than just an ML Engineer; we are looking for a visionary who is eager to make a substantial impact. The ideal candidate thrives in a fast-paced environment, is deeply curious about AI-driven automation, and is excited to tackle the challenges of deploying machine learning at scale. If you are ready to be part of a dynamic team and drive AI advancements at Quandri, we look forward to welcoming you aboard.

What you’ll do:

• AI Model Development & Deployment: Design, develop, and deploy machine learning models for automation, leveraging modern frameworks like TensorFlow, PyTorch, and scikit-learn. Optimize models for efficiency, scalability, and real-time inference while ensuring seamless integration into production environments.
• ML Pipeline Engineering: Build and maintain scalable end-to-end ML pipelines, ensuring seamless data ingestion, feature engineering, model training, and deployment using tools like MLflow, Kubeflow, or SageMaker.
• GPT Fine-Tuning & LLM Adaptation: Fine-tune GPT-based models and other large language models (LLMs) to align with specific business use cases. Optimize models for domain-specific performance, cost efficiency, and latency while ensuring responsible AI practices.
• MLOps & Model Lifecycle Management: Implement automated training, retraining, and model monitoring systems to track model performance, detect drift, and improve predictive capabilities over time. Establish CI/CD pipelines for ML models to enable seamless deployment and iteration.
• Collaboration & AI Integration: Work closely with data engineers, software developers, and business stakeholders to integrate AI capabilities into automation workflows, aligning models with business needs and real-world applications.
• Data Quality & Feature Engineering: Develop and implement robust data preprocessing, augmentation, and feature extraction techniques to enhance model accuracy and reliability. Ensure compliance with data governance and security standards to maintain model integrity.
• Cloud & System Optimization: Deploy models on cloud-native environments (AWS, GCP, Azure) and optimize inference performance for cost efficiency and low latency using containerization (Docker, Kubernetes) and serverless architectures.
• AI Observability & Performance Monitoring: Develop tools for model observability, including drift detection, explainability, and bias mitigation. Implement real-time monitoring dashboards to track performance metrics and ensure compliance with ethical AI standards.
• Mentorship & Communication: Mentor engineers on AI/ML best practices, conduct knowledge-sharing sessions, and effectively communicate complex AI concepts to non-technical stakeholders.
• Continuous Innovation: Stay up-to-date with emerging AI/ML technologies, proactively enhance existing AI infrastructure, and explore new algorithms, frameworks, and architectures to push the boundaries of automation and AI-driven decision-making.

The right person for this role will have:

• Proven experience in machine learning and data engineering projects, with a track record of building scalable ML pipelines and AI-driven data solutions.
• Proficiency in Python and SQL; familiarity with Scala or Java is a plus.
• Hands-on experience with PyTorch, Scikit-learn, or other ML libraries.
• Experience with Apache Spark for large-scale data processing and analytics, and familiarity with Apache Kafka for real-time data streaming.
• Experience with tools like MLflow, Kubeflow, or SageMaker for model lifecycle management.
• Knowledge of Databricks, Snowflake, Delta Lake, and feature stores for scalable data storage and ML feature engineering.
• Hands-on experience with AWS, Azure, or Google Cloud, with expertise in cloud-native AI/ML services.
• Strong ability to design scalable data architectures, including structured and unstructured data management.
• Expertise in designing and optimizing data pipelines for ML, ensuring clean and structured input data.
• Experience deploying models via Docker, Kubernetes, or serverless ML architectures.
• Understanding of continuous integration and deployment (CI/CD) for ML models, using GitHub Actions, Jenkins, or GitLab CI.
• Proficiency in Apache Airflow, Prefect, or Dagster for automating ML workflows.
• Experience with model drift detection, automated retraining, and AI observability.
• Proficiency in Power BI, Tableau, or Python visualization libraries (Matplotlib, Seaborn, Plotly) for data storytelling.
• Strong analytical problem-solving skills, including explainability techniques for ML models.
• Experience working in Agile teams, using Scrum or Kanban workflows.
• Proficiency in Git, including branching strategies and collaborative development workflows.
• Awareness of ML security risks, data privacy (GDPR, CCPA), and secure coding practices.

Bonus points if you have:

• Bachelor's or Master’s degree in Computer Science, AI, Machine Learning, Data Engineering, or a related field.
• Experience in LLM fine-tuning (GPT models, BERT, T5, etc.), including prompt engineering and domain adaptation.
• Experience with NLP (natural language processing), reinforcement learning, and generative AI models.
• Understanding of the insurance industry, including familiarity with insurance products, underwriting, and claims management processes.
• Experience in Databricks for ML and data engineering, optimizing scalable pipelines and real-time AI applications.

Our guiding principles:

• Customers at the core. We put the customer at the center of all we do. At a basic level, we believe business success comes down to talking to customers and building something they want. We don’t listen to customers and just take what they say blindly, but we think critically about it and build what they need. Customers are the core of everything we do, and our business exists to serve them. We prioritize their needs over all else within the company.
• Move with urgency. There are times when we need to move slowly and deliberately, but we default to acting fast and with urgency. We slow down when necessary, but this should be a deliberate choice. Businesses become more lethargic as they grow, this principle is designed to fight this fact.
• Be curious. We understand the world by being curious and asking why. We aren’t satisfied with surface level understanding, and seek a deeper understanding of why things are the way they are. Don’t take someone’s word for it or the answer “because that’s how we do it.” Understand why and dig deep.
• Excellence in execution. We know that what separates good from great is a high level of execution. We commit ourselves to excellence in everything that we do, from delivering an amazing product to writing a great email.
• Act like an owner. We’re all owners of the business and act like it. We follow through on commitments, own our results and think long-term.
• Fight for simplicity. The law of increasing functional information states that systems evolve to become more complex over time. At Quandri, we believe there is sophistication in simplicity; as such, we intentionally fight for streamlined solutions and are committed to the uncomplicated.

Compensation and benefits:

• The range for base pay is 100$k - 125$k which is dependent on level of experience, performance and choice of stock option compensation
• Employee stock options based on experience level
• Comprehensive health benefits, including Lifestyle Spending Account
• Four weeks of paid vacation per year
• Work anywhere in the world for 60 calendar days of the year

Quandri is dedicated to fostering a diverse and inclusive workplace. As an equal opportunity employer, Quandri adheres to Canadian labour laws and does not engage in discrimination based on race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or any other status protected under Canadian law.

Don’t let imposter syndrome stop you from applying. Great people sometimes don’t have the “right” experience. If you think that you’ll be amazing at this role then we encourage you to apply.