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About the Role
The Machine Learning Infrastructure & Platform team builds foundational architecture for AI and GenAI initiatives. This role focuses on evolving MLOps foundations into a scalable LLM serving and routing platform, enabling Data Scientists and Engineers to host LLMs, optimize inference, manage GPU infrastructure, and benchmark performance.
Responsibilities
- Transition traditional ML lifecycle and serving patterns into state-of-the-art LLM inference engines and GPU orchestration systems.
- Design low-latency routing frameworks to dynamically direct requests across managed cloud providers and self-hosted open-source models.
- Architect and manage high-performance GPU serving environments on Kubernetes using engines like vLLM, Ray, and Triton.
- Build automated Evals and observability frameworks to validate model quality, latency, and drift against production requirements.
- Partner with product engineering and data science teams to build framework-agnostic platform tooling that abstracts infrastructure complexity.
- Improve price-performance across self-hosted and managed inference by optimizing capacity, utilization, batching, routing, and model selection.
Requirements
- 7+ years of software engineering experience in ML Infrastructure, MLOps, ML Tooling, or Data Platform engineering.
- Deep experience in MLOps/ML Platform practices: Proven track record building and operating self-serve ML platforms, model registry workflows, experiment tracking, or production serving infrastructure (Kubeflow, MLflow, Ray, Triton, SageMaker).
- Advanced proficiency in Python, container orchestration via Kubernetes, infrastructure-as-code (Terraform), and cloud provider ecosystem (AWS).
- Strong appetite to specialize in LLM serving: A genuine desire to leverage existing MLOps skillset to tackle LLM-specific challenges (vLLM, model routing, prompt engineering tooling, vector databases, GPU memory optimization, or LLM evaluation frameworks).
- Experience designing highly available, observable microservices (e.g., FastAPI) handling real-time, low-latency requests.
- Proven capability to lead architectural roadmaps, guide multi-functional projects with high autonomy, and maintain complex platform systems for the long run.
- Direct experience serving open-source Large Language Models in production (vLLM, SGLang, TensorRT-LLM, Dynamo).
- Hands-on work with CUDA, GPU partitioning, or distributed inference frameworks (Ray Serve).
- Familiarity with vector search and retrieval engines (Elasticsearch, Qdrant, Pinecone).
Skills
- MLOps
- ML Platform
- LLM inference
- GPU orchestration
- Kubernetes
- vLLM
- Ray
- Triton
- LiteLLM
- AWS Bedrock
- Python
- FastAPI
- Terraform
- AWS
- Kubeflow
- MLflow
- SageMaker
- CUDA
- Ray Serve
- Elasticsearch
- Qdrant
- Pinecone
Location
- Toronto, Canada
- North America
Work Type
- Hybrid
Experience Level
- 7+ years of software engineering experience
Benefits
- Top-tier health benefits and life insurance
- Long-term group savings with employer match
- 20 vacation days
- 4 wellness days
- Unlimited sick and mental health days per year*
- 90 days away: work outside Canada for up to 90 days per year*
- Employee resource groups
About the Company
- Wealthsimple is Canada’s leading financial innovator, offering a full suite of simple, sophisticated financial products across managed investing, do-it-yourself trading, cryptocurrency, tax filing, spending and saving.
- The company serves over 4 million Canadians and holds over $155 billion in assets under administration.
- Founded in 2014 by financial experts and technology entrepreneurs.
- We move quickly and build thoughtfully, 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.
Equal Opportunity
- 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.
- 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.
- 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.