Senior Machine Learning Operations Engineer

The Senior Machine Learning Operations Engineer plays a pivotal role in designing, developing, and maintaining a robust software development infrastructure that supports the Software and ML Development teams at Cambrian.

At Cambrian, our vision is to make matter programmable through the integration of robotics and AI. We’re committed to creating an ecosystem of hardware and software tools that enable manufacturers worldwide to benefit from intelligent automation, reducing the need for manual labor and empowering humans to focus on more meaningful tasks.

The Senior Machine Learning Operations Engineer plays a pivotal role in designing, developing, and maintaining a robust software development infrastructure that supports the Software and ML Development teams at Cambrian. As an integral member of the development team, this individual possesses a diverse set of software development skills and extensive experience in managing Cloud, Remote, and Local computer networks. The primary responsibility is to establish and manage a streamlined system where the entire software stack can be built, tested, versioned, deployed, and released in a structured, predictable, and efficient manner.

As Cambrian transitions from its startup phase to a scale-up, there is a strategic emphasis on investing in ML Ops to facilitate team growth and accelerate development. This presents an exciting opportunity for the ideal candidate to contribute their expertise, innovative ideas, and take ownership of the software infrastructure.

Key Responsibilities:

  • Infrastructure Management: Provision and maintain internal and external cloud infrastructure, ensuring uptime, performance, and security.
  • Continuous Integration and Deployment (CI/CD): Implement, maintain, and optimise CI/CD pipelines for both software and ML models.
  • Model Training and Deployment automation: Automate the training and deployment of ML models into production on new data or configurations.
  • Collaboration: Work closely with software developers and data scientists to optimise their workflows and ensure smooth deployments.
  • Data Management: Ensure efficient and reproducible data pipelines, manage data quality, and validation.
  • Documentation: Maintain detailed documentation on MLOps processes, model details, and best practices.
  • Security: Implement best practices to ensure data, application security.
  • Disaster Recovery: Design and implement strategies for data and application backups and recovery.
  • Ad hoc development and project tasks

 

What we are looking for:

  • Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
  • Proven experience +5 years in MLOps environments, with a track record of implementing CI/CD processes and ML workflows.
  • Technical Skills: Proficiency with cloud platforms and common tools ML ops tools
  • Strong understanding of containerisation, infrastructure automation, and machine learning development and deployment.
  • Passion for robotics and the tech challenge

 

Pay & Benefits

At Cambrian, base pay is one part of our total compensation package and is determined within a range. The base pay range for this role is between £70,000 and £90,000 and your base pay will depend on your skills, qualifications, experience, and location.

This can be a hybrid role, requiring at least ~>60% on-site presence.

Interview Process

01

Meet with Talent

Here you will have an introduction where we learn more about each other and talk through the Cambrian journey, past, present and future and see if our ambitions align with yours

02

Technical assessments

This is where we give you a take-home task or conduct a scenario interview.

03

Role assessments

Deeper technical interviews and introductions with other Cambrians - we want to make sure you find the fit just as much as we do.

04

Meet the Team + Robots in our HQ

A chance to meet the hiring and senior management team.

05

The offer

If everything goes well, this is where we invite you to join the team at Cambrian.

Apply

Tell us why you’d be good fit for the MLOps role.

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Hybrid working

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Full-time

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Flexible hours

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Work equipment

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Education stipend

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Equity

Company retreats

Flexible vacation