Machine Learning Operations Manager

Key Skills

Ansible, Argo, Continuous Integration & Continuous Delivery - CI/CD, Docker, Flux, Git, Github, Jenkins, Kubernetes-K8s, Linux Operating system, Puppet, TeamCity, Terraform

Job Description

We strongly encourage people of colour, lesbian, gay, bisexual, transgender, queer and non-binary people, veterans, parents, and individuals with disabilities to apply. Bumble is an equal opportunity employer and welcomes everyone to our team. If you need reasonable adjustments at any point in the application or interview process, please let us know.

In your application, please feel free to note which pronouns you use (For example - she/her/hers, he/him/his, they/them/theirs, etc).

Bumble is looking for MLOps Engineers to join our MLOps Core team who is responsible for developing and maintaining the tools, processes and infrastructure enabling our Machine Learning delivery lifecycles. The role will be focussed on making improvements to the MLOps platform we’re using to productionize ML models and Workflows.

We are looking for a strong hands-on Team Lead with a DevOps/SRE or similar experience and a passion for delivering impact on our products with Machine Learning. With millions of images and messages exchanged on our platform every day, there is a wealth of opportunity to make a real difference in this role and help people to find love all over the world!

KEY ACCOUNTABILITIES

• Determine the most appropriate route to get a model from the prototype phase to production so that it can begin delivering business value whilst minimising risk, time and cost.

• Be a key driving force behind and contributor to discussions supporting decision making with respect to system infrastructures (e.g. cloud or internal), architectures, libraries, workflows etc.

• Drive hardware capacity planning initiatives to ensure optimal resource allocation and performance for mission-critical machine learning operations (MLOps) systems

• Understand the existing infrastructures for productionising machine learning models along with their limitations in order to proactively drive forward improvements to processes.

• Support the data science team development lifecycle

• Support the data science team in the productionisation work required for a number of different projects (computer vision, NLP, recommender systems etc).

• Build frameworks that are highly generic in order to minimise time to production and enhance iteration capabilities.

• Collaborate with cross-functional engineering teams in order to take a project to product-ready status.

• Understand the requirements of a project from a technical production perspective in order to support data science team members in the prototyping phase.

• Establish and develop appropriate monitoring infrastructures for production models

REQUIRED SKILLS AND KNOWLEDGE

• Knowledge of service engineering practices from both a theoretical and practical implementation standpoint.

• Solid software engineering skills and confidence in programming and scripting (strong Python essential).

• Strong understanding of Linux

• Strong understanding of Docker and Kubernetes

• Knowledge of Git, GitOps practices, ArgoCD/Flux

• Knowledge of CI/CD tools such as Jenkins, Teamcity, GitHub Actions, etc.

• Knowledge of DevOps tools such as Puppet, Ansible, Terraform.

• Knowledge of networking and protocols. Understanding of service-mesh technologies (e.g istio).

• Proven ability to translate complex concepts into a digestible format understood by non-technical stakeholders.

• Ability to work independently. Ability to work in a team environment.

• Appetite for learning and interest in DS/ML field