Senior MLOps Engineer

Bumble is looking for MLOps engineers to join our team and play a key role fulfilling our mission to create a world where all relationships are healthy and equitable. Concretely, this means maintaining and improving the MLOps platform that supports the lifecycle of state of the art machine learning models, developed by several teams at Bumble Inc (Recommendations, Trust & Safety, etc).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! The ideal candidate combines strong technical skills, extensive experience in managing Kubernetes clusters along with a passion for ML.WHAT YOU WILL BE DOINGMaintain and improve the MLOps platform allowing us to serve predictions at massive scale and iterate faster on all our modelsAdminister and manage the GPU-powered MLOps Kubernetes clustersBe part of the on call rota to support smooth operation of the MLOps platform and the health of all our ML servicesImprove the MLOps platform in terms of processes, performance and testingResearch and experiment with the latest MLOps technologies and inference frameworks to unlock capabilities for all ML engineers in the companySupport the efforts of ML engineering and product teamsMentor and coach team members on DevOps and ML engineering best practicesWE’D LOVE TO MEET SOMEONE WITHDeep understanding of Kubernetes infrastructure and experience administering GPU enabled Kubernetes clusters at scaleExperience working with Docker and containerised applicationsExperience with at least one programming language such as Python, Golang etc.Experience with CI/CD tooling such as ArgoCD, GitHub Actions etc.Experience configuring and maintaining monitoring systems such as Grafana, Prometheus etc.Experience with IaC tooling, e.g. TerraformComfortable in reacting to incidents and be part of an on call rotationGood understanding of machine learning model development life cycle processes and tools: ML model development and experimentation, training pipelines, model serving and monitoringAbility to work collaboratively and proactively in a fast-paced environment alongside engineers, scientists and non-technical stakeholdersA passion for keeping up with the latest ongoings in DevOps and MLOps communitiesA curious mind, self-starter and endlessly keen to learn and develop themselves professionallyAN ADDED BONUS IF YOU HAVEExperience configuring and maintaining tools supporting the ML model lifecycle (e.g. Kubeflow)Experience configuring and maintaining feature and embedding storage systemsExperience with cloud infrastructures - GCP is a plus

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