Moz

Senior Machine Learning Engineer

Moz

full-time

Posted on:

Origin:  • 🇨🇦 Canada

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Salary

💰 $160,000 - $170,000 per year

Job Level

Senior

Tech Stack

AWSCyber SecurityDistributed SystemsDockerDynamoDBEC2MicroservicesPythonPyTorchScikit-LearnTensorflowTerraform

About the role

  • Implement and optimize LLM-based features including prompt engineering, fine-tuning, and RAG systems
  • Design, develop, and train machine learning models for various business applications (classification, regression, NLP)
  • Conduct rigorous model evaluation using appropriate metrics and cross-validation strategies
  • Develop evaluation pipelines for both traditional ML models and LLM applications
  • Create reproducible experimentation frameworks for model iteration and improvement
  • Build ensemble methods and optimize model architectures for performance/accuracy trade-offs
  • Deploy ML infrastructure on AWS (SageMaker, Bedrock, EC2) and develop cost-effective solutions
  • Use Docker and Terraform to manage ML environments
  • Build real-time and batch inference systems for ML models
  • Create data pipelines and feature stores for model training
  • Set up vector databases and RAG systems for LLM applications
  • Implement monitoring and alerts for models in production
  • Manage both traditional ML workloads and LLM API integrations
  • Stay current with ML advancements, especially in LLMs
  • Collaborate with engineering teams to integrate ML features into products
  • Participate in code reviews ensuring high-quality, maintainable ML code
  • Contribute to technical documentation and knowledge sharing
  • Support other engineers in understanding and using ML systems
  • Partner with product teams to scope and deliver ML features

Requirements

  • 6+ years of experience in machine learning engineering or data science
  • 3+ years of production Python development with experience in ML frameworks (PyTorch, TensorFlow, Scikit-learn, XGBoost)
  • Experience in building RESTful APIs and microservices
  • Experience with AWS services including SageMaker, S3, DynamoDB, and EC2
  • Experience with Bedrock or similar LLM services
  • Experience with Docker and containerization
  • Experience with the full ML lifecycle: problem framing, data analysis, model development, evaluation, and deployment
  • Strong software engineering fundamentals and design patterns
  • Experience with version control (Git) and collaborative development
  • Understanding of distributed systems and scalability
  • Bonus: PhD or research experience
  • Bonus: Deep experience in applied ML areas (NLP, deep learning, Bayesian methods, reinforcement learning, clustering)
  • Bonus: Experience with Infrastructure as Code (Terraform)