Staff MLOps Developer, Wealthsimple
Risk
Salary not provided
AWS
Docker
Kubernetes
Python
Kafka
Airflow
Tensorflow
Redshift
Scikit-Learn
Snowflake
PyTorch
Sagemaker
Git
Senior and Expert level
Remote from Canada
Professional investing services made simple
Be an early applicant
Professional investing services made simple
1001+ employees
FintechB2CPersonal financeInvestingFinancial ServicesSocial Impact
Be an early applicant
Salary not provided
AWS
Docker
Kubernetes
Python
Kafka
Airflow
Tensorflow
Redshift
Scikit-Learn
Snowflake
PyTorch
Sagemaker
Git
Senior and Expert level
Remote from Canada
1001+ employees
FintechB2CPersonal financeInvestingFinancial ServicesSocial Impact
Company mission
To help everyone achieve financial freedom.
Role
Who you are
- 5+ years of experience as a data scientist and/or machine learning engineer, with a focus on advanced models in finance
- Expertise in Python, with a strong familiarity with key data science & machine learning libraries and tools. (PyTorch/TensorFlow/LightGBM/Scikit-Learn)
- Ownership of the design and development of an ML platform: demonstrated experience in productionalizing ML models, with a track record of owning the platform for ML model development, from prototyping to deployment and maintenance
- Solid knowledge of data engineering and preprocessing, including handling large datasets, ensuring data quality, and managing data pipelines
- Excellent communication skills, with the ability to convey complex ideas in a simple, clear, and engaging manner for a variety of audiences
- Outstanding interpersonal skills, demonstrated through the ability to thrive in highly collaborative settings and contribute positively to team-oriented environments
- Familiarity with key deep learning and LLM concepts such as tokenization, fine-tuning, sequence prediction, embedding
- Experience optimizing the runtime of training or inference on GPUs including parallel training strategies, optimal batching, mixed precision
- Familiarity with AI ethics and responsible AI practices, with a commitment to ensuring that our solutions are not only effective but also ethical and fair
- Experience with relevant data platforms like kafka, snowflake, redshift
- Experience with ML development platform and cloud environments like AWS and SageMaker
- Experience with distributed systems, containerization (Docker/Kubernetes), and workflow orchestration (Airflow)
- Experience with CI/CD tools and working with Git
What the job involves
- The quantitative risk team is responsible for the data and models that protect Wealthsimple and its customers from fraud, credit and market events
- We are looking for a founding MLOps developer to lead the development of our AI/ML pipelines
- This function plays a critical role in accelerating our adoption of AI, automating our training and tuning pipelines, and hardening our ML infrastructure against threats
- This individual will leverage a deep understanding of ML operations, engineering, and AI and the effective application of these tools in finance
- They will apply this knowledge to enhance our end-to-end modeling infrastructure and help us deploy and maintain advanced models
- This role offers the unique opportunity to work on high-impact projects that directly contribute to the security and efficiency of our platform
- Automated Model Recalibration Pipeline: Implement and deploy an automated pipeline for model recalibration and re-fitting across key models, starting with those for fraud detection
- Your work will ensure our models maintain optimal performance through systematic refreshing as market conditions evolve and threat actors' tactics change
- Wealthsimple AI Foundation Model: Contribute to the continued development and productionization of our foundational AI model
- This project requires both data science expertise and engineering rigour to transform how we understand our clients
- You will apply advanced machine learning techniques while building scalable training pipelines and optimizing model performance
- Your work will involve designing systems that effectively process diverse client signals (trades, transfers, clicks, purchases) into a unified high-dimensional embedding, then engineering these solutions from development to production
- This versatile embedding will serve as the core intelligence layer for numerous downstream applications, including:
- Fraud Detection: classify signals associated with known fraud types
- Client Segmentation: group clients based on their unique behaviours
- Outlier Detection: identify abnormal behaviours and patterns beyond traditional fraud, enhancing our ability to maintain a secure and trustworthy platform
- Model Explainability: Drive model explainability (xAI) initiatives to ensure ML models meet regulatory requirements and can be successfully deployed to production environments
- Develop and implement interpretability techniques that make complex models transparent to stakeholders, satisfying compliance standards while enabling faster model approval and deployment cycles
- MLOps Security & Infrastructure Expertise: Lead the implementation of best practices in our machine learning operations to create a secure, scalable infrastructure
- You will architect robust ML systems that protect against cybersecurity vulnerabilities including data poisoning, model evasion, and adversarial attacks, while ensuring our decision infrastructure remains performant and maintainable at scale
Our take
Wealthsimple is one of a crop of robo-advisors trying to democratize investment management. These companies are disrupting the industry by automating many of the processes involved in investment removing the need for human advisors.
But while other so-called robo-advisors are focused on courting the high end of the market, Wealthsimple is trying to provide the same types of tools and advice to all users, regardless of the amount they have to invest.
Wealthsimple does have higher fees than some other robo-advisors such as Betterment and Charles Schwab. However, the company believes that it provides a more user-friendly platform, tailored to millennials who will be attracted to Wealthsimple due to its ease of use and social impact offerings.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
Top investors
-10% employee growth in 12 months
Company
Funding (last 2 of 9 rounds)
Oct 2025
$394m
LATE VC
May 2021
$537.2m
LATE VC
Total funding: $1.2bn
Company benefits
- Retirement savings matching plan through Wealthsimple Work
- Company equity for full-time employees
- Access to Premium Client perks, including personal finance learning & advice and a boosted interest rate on deposits
- Paid parental leave (6 months topped up to 100% of salary)
- 20 vacation days per year
- 90 Days Away program
- Top-tier health benefits
- Life & disability insurance
- Generous sick and mental health days
- Employee resource plan including services like RMT, psychology, and physiotherapy
- Up to $5,000 per year for mental health supports
- Gender Affirmation benefits
- Up to $1,500 per year for professional development
- Up to $1,500 per year for wellness and home office expenses
- Employee resource groups
- Social impact hours
Company HQ
Fashion District, Toronto, ON
Leadership
Michael Katchen
(CEO)Previously worked as a Business Analyst at McKinsey and then at Ancestry.com as Country Director for Canada.
Share this job
View 16 more jobs at Wealthsimple