Senior Artificial Intelligence Engineer, League
CA$152-175k
More information about location
Consumer experience platform for healthcare
Open for applications
Consumer experience platform for healthcare
501-1000 employees
Open for applications
CA$152-175k
More information about location
501-1000 employees
Company mission
To power next-generation healthcare experiences with a platform built to enable CX transformation.
Role
Who you are
- Deep Technical Expertise: Extensive hands-on experience in software engineering and a strong understanding of the entire machine learning lifecycle
- Platform-Level Thinking: Proven ability to design and build scalable, distributed systems, ideally for machine learning or data-intensive applications
- MLOps Mastery: Demonstrated experience with MLOps tools and practices, including CI/CD for machine learning, model versioning, and feature stores
- Cloud Proficiency: Expertise with public cloud platforms (e.g., AWS, GCP, Azure) and a solid understanding of containerization and orchestration technologies like Docker and Kubernetes
- Data Fluency: A strong grasp of data engineering concepts, including data pipelines, data warehousing, and distributed data processing frameworks
- Cloud Platforms: Extensive experience with GCP and/or AWS, including core compute, storage, and networking services
- Programming Languages: Expert-level proficiency in Python, familiarity with Go for backend services is a bonus
- Data & AI Frameworks: Experience with big data processing platforms like Apache Spark, Apache Beam, Hadoop, etc
- Familiarity with modern machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn
- MLOps & Orchestration: Deep understanding of MLOps principles and hands-on experience with tools like Kubeflow, MLflow, LangChain or LLamaIndex
- Experience with workflow orchestration tools like Airflow or a similar platform
- DevOps & Infrastructure:Expertise in containerization and orchestration using Docker and Kubernetes
- Hands-on experience with Infrastructure as Code (IaC) tools like Terraform
- Data Systems: Experience with both relational and NoSQL databases, and familiarity with data warehousing and streaming technologies. Experience with vector databases (Pinecone, ChromaDB, Mongo) and retrieval strategies (chunking, hybrid search, re-ranking) a definite bonus
- Ensure access management is performed in compliance with the employee's role and responsibilities
- Responsibility and accountability for executing League's policies and procedures within the department/ team
- Notification of HR, Legal, Compliance & Security of any incidents, breaches or policy violations
- Compliance with Information Security Policies
What the job involves
- We are seeking a Senior AI Engineer to join our AI & Data Group on the AI Orchestration team
- This role is responsible for designing, building, operating and scaling the orchestration layer that powers League’s AI-driven healthcare experiences
- Sitting at the intersection of AI systems design, ML productionization, and distributed software engineering, you will take the probabilistic capabilities of large language models and engineer them into reliable, measurable, and scalable product experiences
- This is a hands-on senior engineering role for a practitioner who moves beyond prototypes to own the hard problem of making AI work at scale
- You will translate leading edge AI innovation into reliable, secure, and compliant systems
- Your work will directly impact how AI is operationalized across League’s digital health ecosystem—ensuring systems are scalable, observable, performant, and safe in a regulated environment
- Design and build production-grade AI systems, including RAG pipelines, multi-step agents, and LLM-powered features
- Make principled architecture choices regarding RAG vs. fine-tuning and agentic loops vs. simpler call-and-response patterns
- Architect for long-term maintainability, ensuring systems fail gracefully and handle non-deterministic outputs predictably
- Build comprehensive evaluation and observability frameworks to measure model accuracy, grounding, and quality drift
- Implement automated test suites and "LLM-as-judge" pipelines to catch defects before they reach production
- Set quality standards for AI components and drive improvements based on human feedback loops
- Create production-quality Python services to wrap AI logic into secure microservices
- Leverage AI coding assistants (Claude Code, Codex, Cursor, etc) to write the majority of your code, while still retaining ownership and deep understanding of the product created
- Own the model lifecycle, including versioning prompts as first-class code artifacts and monitoring for performance degradation
- Manage the economics of LLM usage, balancing model performance against latency and cost
- Partner with Product, Data Science, and Backend teams to translate ambiguous requirements into technical specifications
- Mentor junior engineers on AI craft, including embedding selection, vector store design, and prompt engineering precision
- Actively reduce knowledge concentration by contributing to shared AI tooling and documentation
- Contribute to roadmap planning and longer-term AI architecture decisions
- Establish and uphold standards for performance, security, privacy, and data governance within AI systems
Our take
Health benefits are a top requirement for anyone seeking a new job in the US today, but classic one-size-fits-all plans are no longer regarded fit for service. League's platform - for payers, providers, consumer health businesses, and employees - connects all benefits in one HIPAA-compliant place, to enable all bodies involved with health benefits to provide better customer healthcare experiences.
League is already one of the bigger players in the employee wellness space, having capitalised on the recently accepted correlation between employee health and productivity to secure huge clients like Uber, Shopify and Unilever. The company has seen significant investment, which will support the scaling of its platform-as-a-service offering. With more than 63 million contracted users, it is securing its position as the leading digital infrastructure for an integrated health ecosystem.
Freddie
Company Specialist at Welcome to the Jungle
Insights
-4% employee growth in 12 months
Company
Funding (last 2 of 4 rounds)
Feb 2022
$95m
SERIES C
Jul 2018
$47.1m
SERIES B
Total funding: $171.1m
Company benefits
- Flexible medical and dental plans
- 401(k) and RRSP matching programs
- Health, lifestyle, learning and family spending accounts
- Employee stock option program (ESOP)
- Generous leaves (including parental and sabbatical)
- Flexible time off
Company HQ
Entertainment District, Toronto, ON
Leadership
Michael Serbinis
(Founder & CEO)Founder of DocSpace and Kobo. Experience as CEO for Three Angels Capital and Board Director for Vector Insitute, among other prominent business leadership roles.
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