Machine Learning Infrastructure Specialist

3 days ago


Detroit, Michigan, United States Canopy Full time
Revolutionize Vehicle Security with Canopy

We are seeking a highly skilled Staff MLOps Engineer to join our team at Canopy, a pioneering company in the field of advanced threat detection and deterrence products for vehicles. Our mission is to eliminate vehicle crime and enhance mobility through cutting-edge consumer hardware, aftermarket connectivity, and AI-driven security solutions.

About the Role

This exciting opportunity involves leading the design, development, and implementation of scalable machine learning operations (MLOps) pipelines and infrastructure. As a key member of our team, you will work closely with machine learning engineers, data scientists/engineers, and platform/SRE engineers to streamline and automate data ingestion, model deployment, ensuring high availability, reliability, and performance of AI-driven solutions.

Main Responsibilities:
  • Design and Implement MLOps Services: Automate the deployment and monitoring of machine learning models in production environments using robust MLOps services and pipelines.
  • Collaborate with Engineers: Integrate machine learning models into existing systems and workflows, working closely with engineers to ensure seamless integration.
  • Develop Best Practices: Enforce best practices for CI/CD in machine learning projects, including version control, automated testing, and continuous integration.
  • Monitor Performance: Monitor the performance and health of data and models in production, implementing strategies for retraining, versioning, and rollback when necessary.
  • Optimize Infrastructure: Optimize infrastructure for model training and inference, ensuring scalability, efficiency, and cost-effectiveness.
  • DevOps Integration: Work closely with DevOps teams to ensure seamless integration of MLOps pipelines with cloud or on-premises infrastructure.
  • Mentorship and Guidance: Provide mentorship and guidance to junior engineers and contribute to the development of MLOps strategies and standards across the organization.
Requirements and Qualifications

To be successful in this role, you will need:

  • A Bachelor's degree in Computer Science, Data Science, Engineering, or a related field.
  • At least 8 years of experience in software engineering, data engineering, or a related field, with at least 3 years specifically in MLOps.
  • Strong programming skills in languages such as Python, Java, or C/C++.
  • Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
  • Proficiency in DevOps tools and practices, including Docker, Kubernetes, and CI/CD pipelines.
  • Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
  • Strong understanding of version control systems like Git and experience with infrastructure as code (IaC) tools such as Terraform or CloudFormation.
Preferred Qualifications

Prior experience with advanced MLOps practices such as feature stores, model interpretability, and fairness in AI is desirable. Familiarity with big data technologies like Apache Spark, Hadoop, or Kafka, as well as monitoring and observability tools such as Prometheus, Grafana, or ELK stack, would also be beneficial.

What We Offer

We offer a comprehensive benefits package, including:

  • Comprehensive medical benefits coverage, dental plans, and vision coverage.
  • Health care and dependent care spending accounts.
  • Employee and Family Assistance Program (EAP).
  • Employee discount programs.
  • Retirement plan with a generous company match.
  • Generous Paid Time Off, Sick, and Holidays.
  • Family Leave (Maternity, Paternity).
  • Short- and long-term disability.
  • Life insurance and accidental death & dismemberment insurance.

Base Salary Range: $129,200 - $180,500

Compensation may vary depending on skills and experience.



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