Job Closed
This listing is no longer active.
Building a future where transportation is shared, affordable and carbon-free. Join us! www.li.me/careers
Principal Machine Learning Engineer
Location
Canada
Posted
110 days ago
Salary
CA$192K - CA$264K / year
Seniority
Lead
Job Description
Principal Machine Learning Engineer
Lime
• Drive alignment across teams on ML strategy, standards, and long-term technical direction by serving as a technical leader for Lime’s ML Center of Excellence • Guide recommendations for ML infrastructure, tooling, and architecture (training, serving, feature stores, experimentation, monitoring) • Define and evolve ML development processes, including model review, experimentation rigor, deployment, optimization, and operations • Establish best practices for ML monitoring, observability, alerting, and model performance health in production • Drive reusable feature development patterns and shared ML capabilities that enable teams to move faster and more safely • Partner with platform, data, and product engineering teams to ensure ML systems are reliable, scalable, and cost effective • Identify and prioritize opportunities where ML will improve Lime’s product, operations, or efficiency • Act as a force multiplier by mentoring data scientists and machine learning engineers, raising the quality bar for machine learning across Lime
Job Requirements
- 8+ years of professional experience in software engineering or applied ML
- Fluency in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow) and data tools (e.g. SQL, pandas, spark, airflow)
- Strong foundation in ML fundamentals, including model evaluation, experimentation, optimization, production deployment, and operations
- Strong system design skills and comfort working with distributed systems
- Track record of influencing ML architecture and practices across multiple teams
- Background in domains relevant to Lime (e.g., forecasting, optimization, pricing, marketplace dynamics)
- Prior experience building a ML platform or center of excellence through defining ML standards, governance, or shared tooling at scale
Benefits
- Offers Equity
- Offers Bonus
Related Guides
Related Job Pages
More Machine Learning Engineer Jobs
• Build and maintain CI/CD pipelines for machine learning models • Operationalise model lifecycle processes including versioning, promotion, and rollback • Deploy and operate LangChain and LangGraph based services in production • Implement monitoring for model performance, drift detection, and runtime failures • Ensure traceability, auditability, and compliance readiness in regulated environments • Collaborate with data scientists and ML engineers to support smooth model releases • Optimise reliability, scalability, and security of ML and GenAI workloads
MLOps Engineer – Portfolio Optimisation, Customer Analytics Platform
MadiffLet's make a difference together!
• Design and operate training and deployment pipelines for analytical and optimisation models • Automate model retraining, validation, and promotion processes • Ensure reproducibility and consistency across development, testing, and production environments • Support scalable analytical workloads across cloud platforms • Enable structured exposure of model outputs to LangChain and LangGraph workflows • Monitor performance, stability, and reliability of ML pipelines • Collaborate closely with data scientists and analytics teams to streamline experimentation to production
MLOps, LLMOps Engineer
Irth SolutionsThe Most Complete SaaS Platform for Damage Prevention, Asset Protection and Risk Management
• Design, automate, and operate scalable ML and LLM systems on Irth’s enterprise Lakehouse platform. • Work closely with Data Science, Engineering, and Product teams to deploy reliable, secure, and production-ready ML and GenAI solutions. • Operationalize ML models, build CI/CD pipelines, ensure governance and compliance, and maintain high-performance AI systems.
ML Engineer
Social Discovery GroupTop world’s largest social discovery company uniting 70+ brands with 500M+ users
• Own a project end-to-end: from data and experiments to production and monitoring • Improve existing recommender models (ranking/matching) and iterate via offline evaluation + A/B tests • Build and refine value prediction signals (e.g., LTV@30, first purchase / conversion probability) • Develop training and scoring pipelines; ensure data quality and reproducibility • Deploy models to production (batch/API), package solutions into Docker, follow CI/CD practices • Collaborate with Product and Analytics to define success metrics and turn results into product changes • Share knowledge through code reviews, documentation, and mentoring within the team



