Senior ML Engineer

What you’ll do:

• Design and implement end-to-end ML pipelines from data ingestion to model serving
• Build automated model training, validation, and deployment workflows using CI/CD best practices
• Develop and maintain MLOps infrastructure for model versioning, experiment tracking, and deployment
• Create robust monitoring and alerting systems for model performance, data drift, and system health
• Optimize model inference performance and cost for production workloads
• Collaborate with our Data Engineers to design and implement feature stores and feature pipelines
• Develop automated data quality checks and validation frameworks for ML pipelines
• Build scalable feature engineering workflows that support both batch and real-time inference
• Implement data versioning and lineage tracking for reproducible ML workflows
• Partner with Software Data Scientists to productionize research models and experiments
• Implement frameworks for model evaluation and gradual rollouts
• Design automated retraining pipelines and model refresh strategies
• Establish best practices for model governance, documentation, and compliance
• Work closely with Software Engineers to integrate ML capabilities into existing applications
• Contribute to architectural decisions for scalable, maintainable ML systems
• Participate in code reviews, documentation, and knowledge sharing across teams
• Mentor junior engineers on ML engineering best practices

The right person for this role will have:

• 3+ years of experience in machine learning engineering, MLOps, or related fields
• Strong proficiency in Python for ML development and automation
• Solid SQL skills for data manipulation and analysis
• Hands-on experience with AWS or other cloud platforms (Azure, or GCP) and their ML services
• Experience with containerization (Docker) and orchestration (Kubernetes) for ML workloads
• Proficiency with ML frameworks (scikit-learn, TensorFlow, PyTorch) and MLOps tools
• Knowledge of CI/CD pipelines and infrastructure-as-code principles
• Databricks experience is a plus
• Experience with feature engineering at scale and feature store implementations
• Understanding of model monitoring, observability, and production ML challenges
• Knowledge of data quality frameworks and automated testing for ML systems
• Familiarity with distributed computing frameworks (Databricks or Sagemaker) for large-scale ML
• Experience with experiment tracking and model versioning tools (MLflow, Weights & Biases, etc.)

Bonus points if you have:

• Master's or PhD in Computer Science, Engineering, Statistics, or related quantitative field Experience with real-time ML inference and streaming data pipelines
• Knowledge of AutoML platforms and automated hyperparameter tuning
• Familiarity with model compression, quantization, and edge deployment Experience with multi-cloud or hybrid cloud ML architectures
• Contributions to open-source ML tools or platforms

Our guiding principles:

• Customers at the core. We put the customer at the centre of all we do. At a basic level, we believe business success comes down to talking to customers and building something they want. We don’t listen to customers and just take what they say blindly, but we think critically about it and build what they need. Customers are the core of everything we do, and our business exists to serve them. We prioritize their needs over all else within the company.
• Move with urgency. There are times when we need to move slowly and deliberately, but we default to acting fast and with urgency. We slow down when necessary, but this should be a deliberate choice. Businesses become more lethargic as they grow, this principle is designed to fight this fact.
• Be curious. We understand the world by being curious and asking why. We aren’t satisfied with surface level understanding, and seek a deeper understanding of why things are the way they are. Don’t take someone’s word for it or the answer “because that’s how we do it.” Understand why and dig deep.
• Excellence in execution. We know that what separates good from great is a high level of execution. We commit ourselves to excellence in everything that we do, from delivering an amazing product to writing a great email.
• Act like an owner.  We’re all owners of the business and act like it. We follow through on commitments, own our results and think long-term.
• Fight for simplicity. The law of increasing functional information states that systems evolve to become more complex over time. At Quandri, we believe there is sophistication in simplicity; as such, we intentionally fight for streamlined solutions and are committed to the uncomplicated.

Compensation and Benefits

• Compensation range of $145k to $200k annually
• Employee stock options based on experience level
• Comprehensive health benefits, including Lifestyle Spending Account
• Four weeks of paid vacation per year
• Work anywhere in the world for 60 days of the year

Application Process

• Please submit your resume highlighting relevant experience and include a cover letter explaining your interest in the specific role
• Provide links to relevant projects, GitHub repositories, or publications
• Be prepared to discuss technical projects and problem-solving approaches in interviews