ML Ops Engineer

About the role

Island is shaping the future of work - creating an enterprise workspace for both knowledge workers and agents - where access, security, and AI-powered productivity are built in. Our enterprise agentic control plane boosts productivity across devices, browsers, applications, networks, and data while protecting sensitive information, simplifying access, and helping enterprises scale AI safely.

Behind all of that is a team of people who care deeply about their craft and want to do the biggest and best work of their careers. At Island, character and drive matter more than your resume.

Backed by Coatue Management, Insight Partners, Sequoia Capital, Cyberstarts, and other world-class investors, Island is used by millions of people at some of the planet’s largest enterprises.

Come build with us. Together, we’re changing how the world works.

About the Role

Island’s AI security features run on machine learning models. Some we train ourselves, and some are open-weight models from the community. As an ML Ops Engineer, you will own the path from research to production: packaging, release, serving, and keeping models fast, reliable and cost-efficient. You will work closely with our researchers and backend engineers.

What You’ll Do

- Build and run the infrastructure that serves ML models in production, on AWS SageMaker and other cloud platforms.

- Own the model release flow: packaging, versioning, CI/CD, safe rollout and rollback.

- Host and run open-weight models and LLMs on CPU and GPU.

- Build pipelines for datasets, training jobs and evaluation, together with Research.

- Monitor models in production (latency, errors, quality, drift) and set up alerts.

- Keep compute costs under control: choose the right instance sizes and track GPU spend.

- You have taken ML models to production. You don’t need to train models, but you understand how they are built, evaluated and served.

You Might Be a Fit If

- You have 4+ years in DevOps, platform or backend engineering, with real production ownership

- You have taken ML models to production. You don’t need to train models, but you understand how they are built, evaluated and served.

- You have strong cloud skills, AWS in particular (IAM, S3, networking).

- You have experience with infrastructure as code (Terraform or similar).

- You have built CI/CD pipelines (GitHub Actions or similar) and work with Docker.

- You use AI coding tools in your daily work.

- You are a team player who takes ownership and learns fast.

We’d Love It If You Have

- Hands-on experience with AWS SageMaker (endpoints, training jobs or pipelines).

- Experience with ML services on other clouds (Azure ML, Vertex AI or similar).

- Experience serving LLMs (vLLM, TGI, Triton or similar) and running GPU instances.