Scale Engineer

Software Development and Data Science Internships (Winter 2027)

Intern · Intern · Internship

Toronto, OntarioCAD 74k – 90k3d ago
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Role

What you'll do.

Join Wealthsimple Technologies as a Software Development or Data Science Intern and contribute to products serving 4+ million Canadians managing over $155 billion in assets. This 4-8 month hybrid internship in Toronto offers mentorship-driven opportunities to build impactful features within a high-performing engineering organization that deploys code 100+ times daily while maintaining rigorous automated testing standards. You'll either architect scalable financial systems across product and platform engineering teams, or leverage machine learning and statistical methods to transform complex financial data into actionable insights that shape product decisions across banking, investing, and fraud prevention domains.

Responsibilities

  • Software Development Track: Ship Production Features Across Financial Products: Develop and deploy user-centric features within product engineering teams focused on Investing, Banking, Lending, and Marketing platforms. You'll contribute to the full software development lifecycle—from architecting solutions with React and TypeScript frontends to building Ruby on Rails and Python backends—while collaborating with cross-functional teams and shipping code to production through Wealthsimple's rigorous CI/CD pipeline that executes 100+ deployments daily.
  • Software Development Track: Build Infrastructure and Platform Services: Collaborate with platform engineering teams to design and maintain the microservices infrastructure, Kubernetes orchestration, and data foundations that enable the entire engineering organization to build, test, and scale applications. Contribute to developer tooling, edge computing solutions, and AI platform initiatives while ensuring system reliability, security, and performance through comprehensive automated testing frameworks.
  • Software Development Track: Develop Core Financial Infrastructure: Work on the Book of Records (BOR) team to architect real-time ledger systems and fundamental financial building blocks written in Java and Kotlin. These systems power all product innovations by providing accurate, auditable, and scalable financial transaction processing that underpins Wealthsimple's entire suite of managed investing, trading, and banking services.
  • Data Science Track: Design and Execute Experiments at Scale: Conduct A/B testing and causal inference experiments that directly influence product decisions across banking, investing, and growth initiatives. You'll design experiment frameworks, analyze results using both frequentist and Bayesian statistical approaches, and translate findings into actionable recommendations for product teams serving millions of Canadian clients.
  • Data Science Track: Build Predictive Models for Financial Risk: Develop machine learning models for credit scoring, fraud detection, and anti-money laundering (AML) countermeasures that protect Wealthsimple's clients and platform integrity. Leverage scikit-learn, regression techniques, and decision tree algorithms to analyze production financial datasets, create early warning systems, and continuously optimize model performance against real-world financial risks.
  • Data Science Track: Partner with Product Teams on Data-Driven Innovation: Collaborate closely with Product, Engineering, and business leadership across Data Science teams including Agentic Analytics, Banking & AI Products, Growth & Investing, Credit Analytics, and Fraud Prevention. You'll extract insights from complex financial datasets using Python, SQL, and statistical analysis to directly shape product roadmaps and business strategy affecting millions of users.
  • Take Ownership and Drive Impact: Identify technical and product challenges proactively, propose solutions, and execute improvements with minimal guidance. Demonstrate initiative by shipping code frequently, iterating based on feedback, and making sound decisions about what features deliver the most value versus what should be deprioritized or cut from scope.
  • Collaborate and Communicate Across Teams: Engage in thoughtful technical discussions with experienced engineers and data scientists, contribute to code reviews with constructive feedback, and communicate decisions and trade-offs clearly to stakeholders. Participate in inclusive debates about technical approaches, share learning from your projects, and support team culture through transparent collaboration.
  • Leverage Modern Development Tools and AI: Utilize best-in-class development infrastructure including AWS cloud services (RDS, Aurora, SQS, SNS, S3, Kafka), comprehensive logging and tracing platforms, experimentation infrastructure, and AI-assisted development tools. Demonstrate curiosity about emerging technologies and contribute ideas on how AI and new tools can improve development efficiency and code quality.

Qualifications

What we look for.

Technical

  • Software Development Track: Web Framework Proficiency

    Solid understanding of modern web frameworks and full-stack development. For Product Engineering roles, demonstrate competency with React, TypeScript, Ruby on Rails, and Python. For Platform Engineering roles, show familiarity with Kubernetes, distributed systems, and infrastructure-as-code concepts. For Book of Records roles, bring Java or Kotlin experience with understanding of real-time transaction processing systems.

  • Software Development Track: Backend Systems and APIs

    Experience building or contributing to RESTful APIs, understanding of database design (SQL and NoSQL), and familiarity with asynchronous messaging patterns. Knowledge of how to structure code for testability, implement logging and monitoring, and collaborate within microservices architectures is essential for deployment velocity at Wealthsimple's scale.

  • Software Development Track: Automated Testing and Deployment

    Understanding of test-driven development practices, unit testing frameworks, and continuous integration/continuous deployment (CI/CD) pipelines. Candidates should be comfortable writing code that ships multiple times per day and understand how automated testing gates ensure security and reliability in financial technology environments.

  • Data Science Track: Python and SQL Proficiency

    Advanced Python programming skills with hands-on experience in scientific Python libraries including Pandas for data manipulation, NumPy for numerical computing, scikit-learn for machine learning, and Jupyter notebooks for exploratory analysis. SQL fluency for querying, joining, and aggregating large production datasets across multiple tables and schemas.

  • Data Science Track: Statistics and Experimental Design

    Strong foundation in statistical theory covering both frequentist and Bayesian approaches, hypothesis testing, confidence intervals, and p-values. Practical experience designing and analyzing A/B tests, understanding statistical power, sample size calculations, and multivariate testing. Familiarity with causal inference methods to distinguish correlation from causation in observational data.

  • Data Science Track: Machine Learning and Predictive Modeling

    Hands-on experience building and evaluating machine learning models including linear regression, logistic regression, decision trees, random forests, and ensemble methods. Understanding of model validation techniques, cross-validation strategies, hyperparameter tuning, and how to assess model performance through appropriate metrics (accuracy, precision, recall, AUC) for different business objectives.

Education

  • Current Enrollment or Recent Graduation

    Enrolled in a Canadian post-secondary institution (university or college) pursuing a degree in Computer Science, Engineering, Mathematics, Statistics, Physics, or related technical field. Alternatively, accepted recent graduates (within 6 months of graduation date) or completers of accredited technical bootcamps with demonstrated programming or data science competency.

  • Strong Academic Foundation

    Demonstrated competency in core computer science or mathematics concepts through coursework or bootcamp completion. Strong academic performance (GPA consideration) and completion of relevant technical coursework in data structures, algorithms, statistics, or machine learning provides a competitive advantage.

Experience

  • Minimum 2 Paid Work Terms

    Must have completed at least 2 paid software development or data science internships, co-op placements, or contract positions. This experience demonstrates professional communication, ability to work within existing codebases, understanding of software development practices, and collaboration with engineering teams.

  • Ownership and Execution Track Record

    Prior internship or work experience showing evidence of taking initiative beyond assigned tasks, shipping features to production, contributing to code reviews, and receiving positive feedback from engineering managers. Examples of identifying and fixing bugs, proposing process improvements, or leading small technical initiatives strengthen candidacy.

  • Familiarity with Modern Development Practices

    Exposure to agile development methodologies, version control systems (Git), code review processes, and automated testing practices through previous roles. Experience in fast-paced startup or growth-stage engineering environments, or demonstrating comfort with high-frequency deployment practices through portfolio projects, is advantageous.

Skills

Required

  • Software Development Track: TypeScript/JavaScript Programming

    Proficient in TypeScript or JavaScript with ability to write clean, well-structured frontend code using React for user interface development and understanding of component lifecycle, state management, and modern ES6+ syntax patterns used in Wealthsimple's product applications.

  • Software Development Track: Backend Language Competency

    Strong programming skills in Ruby on Rails, Python, Java, or Kotlin depending on team assignment. Ability to write efficient, maintainable code that integrates with existing systems, handles errors gracefully, and performs well under production load conditions.

  • Software Development Track: Database and SQL Knowledge

    Solid understanding of relational database concepts, ability to write SQL queries for data retrieval and manipulation, understanding of indexing and query optimization, and familiarity with how database operations impact application performance in financial systems handling billions of transactions.

  • Software Development Track: Version Control and Collaboration

    Proficiency with Git version control, understanding of branching strategies, code review participation, and ability to collaborate on shared codebases without conflicts. Comfort with peer code review processes and receiving constructive feedback on technical implementation.

  • Data Science Track: Statistical Modeling

    Ability to select appropriate statistical tests and models for specific business questions, implement statistical analyses accurately, and interpret results with proper confidence intervals and uncertainty quantification. Understanding of when frequentist versus Bayesian approaches are most suitable for different analytical scenarios.

  • Data Science Track: Data Manipulation and Cleaning

    Expert-level proficiency with Pandas for data transformation, aggregation, and reshaping. Ability to identify and handle data quality issues, merge multiple data sources, and prepare datasets for analysis through efficient vectorized operations rather than slow loops.

  • Data Science Track: Model Evaluation and Validation

    Deep understanding of overfitting, underfitting, and the bias-variance tradeoff. Proficiency with cross-validation techniques, learning curves, and appropriate evaluation metrics for classification (precision, recall, F1, AUC-ROC) and regression problems to ensure models generalize to production scenarios.

  • Problem Solving and Debugging

    Strong analytical thinking ability to break down complex problems into manageable components, develop systematic debugging strategies, and persist through challenges. Comfortable using debugging tools, logging statements, and methodical investigation to identify root causes of production issues.

  • Communication and Documentation

    Ability to explain technical decisions, trade-offs, and implementation choices clearly to both engineers and non-technical stakeholders. Comfortable writing concise documentation, commenting complex code, and presenting findings or analyses to team members and leadership.

  • Self-Directed Learning and Curiosity

    Proactive approach to acquiring new technical skills when needed, asking clarifying questions when uncertain, leveraging documentation and AI tools to find solutions, and demonstrating genuine curiosity about financial technology concepts, distributed systems, or machine learning applications.

Preferred

  • Software Development Track: AWS Cloud Platform Experience

    Nice to have

    Prior hands-on experience with AWS services including RDS/Aurora for managed databases, SQS/SNS for messaging, S3 for object storage, and Kafka for event streaming. Understanding of cloud deployment patterns, managed services, and infrastructure-as-code tools accelerates ramp-up time.

  • Software Development Track: Kubernetes and Container Orchestration

    Nice to have

    Familiarity with Docker containers, Kubernetes orchestration concepts, or experience deploying applications in containerized environments. Knowledge of how microservices are deployed, scaled, and monitored at production scale demonstrates platform engineering aptitude.

  • Software Development Track: Financial Systems Knowledge

    Nice to have

    Any exposure to financial technology concepts including transaction processing, ledger accounting, settlement systems, or regulatory compliance considerations. Understanding of financial industry standards (ISO 20022, FIX protocols) or experience with fintech companies provides valuable context.

  • Data Science Track: Feature Engineering Expertise

    Nice to have

    Experience creating novel features from raw data that significantly improve model performance, understanding of domain-specific feature creation in finance (e.g., rolling averages, momentum indicators, customer lifetime value proxies), and ability to identify which features matter most for prediction tasks.

  • Data Science Track: Production ML Systems

    Nice to have

    Exposure to MLOps practices, model deployment pipelines, or experience monitoring model performance in production. Understanding of technical debt in machine learning systems, model monitoring, and retraining strategies demonstrates readiness for enterprise data science.

  • Data Science Track: Advanced Statistical Methods

    Nice to have

    Familiarity with causal inference techniques (instrumental variables, propensity score matching, difference-in-differences), time series analysis, or Bayesian hierarchical modeling. Knowledge of how to handle confounding variables and hidden biases in observational financial data.

  • AI and Automation Tool Proficiency

    Nice to have

    Comfortable using AI-assisted coding tools (GitHub Copilot, Claude, ChatGPT) to accelerate development velocity, understanding when to use AI assistance and when manual coding is necessary. Demonstrated curiosity about AI applications in your specific domain (software development or data science).

  • Open Source Contributions

    Nice to have

    Experience contributing to open source projects, maintaining personal GitHub repositories with meaningful projects, or participation in coding competitions. Demonstrates ability to work within established codebases, collaborate with other developers, and maintain professional code quality standards.

  • Startup or Fast-Paced Environment Experience

    Nice to have

    Prior internships at growth-stage startups, high-frequency trading firms, or other fast-moving technology companies. Familiarity with shipping frequently, iterating rapidly, operating with incomplete information, and adapting to changing priorities provides cultural alignment with Wealthsimple's operating model.

Compensation

Pay and benefits.

Base·CAD 74,000 – 90,000

Full posting

Original listing.

Build something people love

Wealthsimple is Canada’s leading financial innovator. The company offers a full suite of simple, sophisticated financial products across managed investing, do-it-yourself trading, cryptocurrency, tax filing, spending and saving. Wealthsimple currently serves more than 4 million Canadians and holds over $155 billion in assets under administration. The company was founded in 2014 by a team of financial experts and technology entrepreneurs, and is headquartered in Toronto, Canada.

We're proud of what we've built — and we're just getting started. Read our Culture Manual and learn more about how we work.

Internships @ Wealthsimple

Location: Toronto, ON (Hybrid)

We offer 4-8 month internships designed for co-op students, non-co-op students, and recent graduates who are ready to ship impactful work. You'll join a high-performing team where you'll contribute to projects that are fundamentally changing how Canadians manage their money. We're committed to your growth, providing the mentorship and community you need to deepen your craft and leave better than you arrived.

Our internship program operates on a hybrid model out of our Toronto headquarters. You'll be in-office on Wednesdays and Thursdays to collaborate, connect, and learn in person, while maintaining the flexibility of remote work for the rest of the week.

This posting covers two roles: Software Development and Data Science Intern. Please apply to the one that best fits your interests.

Please note: the role you apply for is the role you will be assessed on throughout the interview process. We are unable to accommodate requests to switch between Software Development and Data Science tracks after submission.

Software Development Intern

Our engineering organization spans everything from product-focused pods to financial infrastructure, building the software that powers our entire suite of services. We operate with a platform-as-a-product mindset, leveraging microservices and an agile, high-iteration approach. With rigorous automated testing and a deployment frequency of 100+ times a day, we move fast while staying secure.

Our Teams:

  • Product Engineering: Drives the development of our core offerings across Investing, Banking, Lending & Marketing. This team builds intuitive and impactful features that empower millions to achieve their financial goals. If you're passionate about crafting user-centric solutions and seeing your work make a real difference, this is your team.

    • Main stack: React, Typescript, Ruby on Rails, Python

  • Platform Engineering: Empowers developers by providing the platforms, tools, and infrastructure needed to build and maintain fast, reliable, secure, and scalable systems. Teams include Reliability, Infrastructure, Data Foundations, Dev Tooling, Edge and AI Platform, built with a platform-as-a-product mindset.

    • Main stack: Typescript, Ruby on Rails, Python, Kubernetes, Snowflake

  • Book of Records (BOR): Builds the core financial building blocks powering our products, including a real-time ledger, and enables product teams to innovate with confidence.

    • Main stack: Java, Kotlin

Additional info on our technologies:

  • Python used across automation, data, and internal tooling

  • Hosted on AWS (RDS, Aurora, SQS, SNS, S3, Kafka)

  • Deploying code 100+ times a day with rigorous automated testing

  • Best-in-class tooling for profiling, logging, tracing, and experimentation

Data Science Intern

Data Science at Wealthsimple turns complex, real-world financial data into decisions, products, and experiences that help millions build wealth. We partner closely with Product, Engineering, and business leaders to design experiments, build models, and surface insights that shape what we ship.

Our Teams:

  • Agentic Analytics & Context Engineering

  • Banking & AI Products DSE

  • Growth & Investing Data Science

  • Enablement Data Science

  • Credit Analytics

  • Fraud & AML Countermeasures

Together, these teams cover the full range of our data work, from building the data infrastructure and tooling that power the business, to partnering directly with banking, growth, and investing product teams, to bringing decision science to our operational and financial backbone, to owning the analytics and machine learning behind credit and fraud risk. Wherever you land, you'll be working with real production data to help shape decisions that directly impact millions of clients.

Tech stack:

  • Python-first environment with Pandas, scikit-learn, NumPy, and Jupyter

  • SQL for data access and manipulation across large, production datasets

  • Statistical foundations across both frequentist and Bayesian approaches

  • Machine learning toolkit spanning regression, decision trees, and beyond

  • Experimentation infrastructure for A/B testing and causal inference at scale

Who We're Looking For

  • Take ownership without being asked. See something broken and fix it

  • Ship fast/frequently and know what's worth iterating on (and what to cut)

  • Tackle new, complex problems with curiosity and self-direction

  • Use AI tools in their workflow and are curious about what else is possible

  • Communicate clearly and care about the person on the other end of what they're building

  • Believe that debate, inclusivity, and transparency make for better products

The Interview Process

Applications close Sep 18th at 11:59 PM EST.

1. Application + Video Submission: Apply and submit a short video using the prompt in the application form.

2. Technical Interview: A 1-hour coding interview in the language you chose on your application form.

3. Final Interview: A 30 minute interview with an engineering or data science manager. Bring a project you're proud of and walk through your role, decisions, trade-offs, and lessons learned. We'll also discuss your collaboration style and how you approach learning and growth.

4. Offers: Offers extended!

Eligibility

  • Enrolled in a Canadian post-secondary institution, technical bootcamp, or a new graduate (Within 6 months of your graduation date)

  • At least 2 paid software development/data science work terms completed

  • Legally authorized to work in Canada (Wealthsimple does not provide immigration or relocation support for interns)

Compensation & Equity

🤑 Base salary range: For this role, candidates can expect the following base salary range:

• CAD $74,000 - $90,000

Your current year of study and number of past internship terms will determine your salary band. Actual compensation within each band is based on skills, experience, and academic performance. Exceptional candidates may be considered above the top of their year's range. 🚀

Why Wealthsimple?

🌸 Top-tier health benefits and life insurance

📈 Long-term group savings with employer match, through Wealthsimple for Business

🌴 20 vacation days, 4 wellness days, and unlimited sick and mental health days per year

✈️ 90 days away: work outside Canada for up to 90 days per year

👥 Employee resource groups, including Rainbow (2SLGBTQ), Women of WS, and Black at WS

🌎 We are a hybrid team with over 1,500 employees across North America. The people are one of the best parts of working here: you'll collaborate with incredibly talented, curious, and driven teammates who are deeply committed to doing great work.

ICYMI

Technology & Innovation at Wealthsimple: We move quickly and build thoughtfully. That means we're always looking for better ways to work — whether that's new tools, AI, or rethinking how we approach a problem. We don't expect you to have all the answers, but we do expect curiosity and a willingness to evolve alongside the products we're building.

Inclusion Statement: We're building products for a diverse world, and we need a diverse team to do it well. We strongly encourage applications from everyone, regardless of race, religion, colour, national origin, gender, sexual orientation, age, marital status, or disability status.

Accessibility Statement: We're committed to an accessible hiring experience. If you need any accommodations throughout the interview process, please let us know — we'll work with you to make sure you have what you need. We also welcome any feedback on how we can better accommodate candidates with accessibility needs.

AI in Hiring: We may use artificial intelligence (AI) tools to support parts of our hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our team but don't replace human judgment – all final hiring decisions are made by people. If you have questions about how your data is used, reach out to us.

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