Lead, MLOps & Analytics Engineering (Remote)

CrossTalent; United States; Remote; USD 180,000 - 240,000; Full time

Benefits offered by this job

Remote-first / Global team

Learning budget USD 2,000 per year

Annual compensation review

Travel opportunities for team events

Holiday leave

Job summary

Canonical is seeking an Engineering Manager to lead a distributed team focused on MLOps and analytics. You will mentor engineers, review code, and drive an ambitious engineering roadmap while collaborating with product managers and architects across the company.

You should be fluent with popular open-source ML tools like Kubeflow, MLFlow, and Feast and foster healthy engineering practices, documentation, and high performance.

Qualifications

• Proven track record of software delivery experience.
• Professional Python development experience, preferably open source.
• Strong understanding of the machine learning space and its challenges.
• Experience designing and implementing MLOps solutions.
• Excellent academic record from high school and university preferred.
• Willingness to travel up to 4 times a year for internal events.

Responsibilities

• Manage a distributed team of engineers and its MLOps/Analytics portfolio.
• Organize and lead the team’s processes to achieve objectives.
• Conduct one-on-one meetings with team members.
• Identify and measure team health indicators.
• Interact with a vibrant community.
• Review code produced by other engineers.
• Attend conferences to represent Canonical and its MLOps solutions.
• Mentor and grow direct reports.
• Work from home with global travel for 2 to 4 weeks per year for internal and external events.

Skills

Software delivery

Python development

ML/Machine learning

MLOps

Open source contributions

Education

Strong academic record (high school + university preferred)

Tools

Docker

Kubernetes

Kubeflow

MLFlow

Feast

Job description

Canonical is seeking an Engineering Manager to lead a distributed team focused on MLOps and analytics. You will mentor engineers, review code, and drive an ambitious engineering roadmap while collaborating with product managers and architects across the company.

You should be fluent with popular open-source ML tools like Kubeflow, MLFlow, and Feast and foster healthy engineering practices, documentation, and high performance.