Dear Netflix Talent Acquisition team,
I am keen to apply for the MLOps Engineer role you posted on your careers page. For the past several years my work has centered on MLOps, and I would be glad to put it to work for you.
Before writing I read up on Netflix, and what struck me was your ML platform work and the engineering posts your team keeps sharing on running models reliably in production. It feels like a solid time to join, and I would gladly aim my MLOps experience at it.
Reading the posting, the three areas you value most are CI/CD for machine learning, model deployment and serving and monitoring and drift detection. Those decide whether an MLOps hire delivers, and I have concrete results in each.
On CI/CD for machine learning, I work with Docker, Kubernetes and GitHub Actions. As an MLOps Engineer at Datadog, I built an automated deployment pipeline that took models from commit to production in under an hour. Beyond that, I built the shared MLOps platform the whole ML team now deploys on.
For model deployment and serving, I rely on Kubeflow, Seldon and model registries. In my time as an MLOps Engineer at Datadog, I shipped a canary rollout system that caught two bad models before they reached users.
On monitoring and drift detection, I bring Prometheus, Grafana and Evidently. Working as an MLOps Engineer at Datadog, I set up a drift-detection and alerting stack that caught model decay days before it hurt metrics. On top of that, I wrote the on-call runbook the whole platform team relies on.
I would be glad to walk you through any of this in an interview and lay out why I fit. I am ready to keep models running in production, help the team ship safely, and grow with it.
Thanks for reading, and I hope we can set up a time to talk.
Yours sincerely,
Theo Script
theo.script@gmail.com