Software Engineer - Europe

Remote: Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia 

TubeScience Labs’ mission is to create trusted, scalable, and self-improving AI systems that power the largest performance-based paid social creative video company in the world. TubeScience is Meta's largest creative partner and AppLovin's #1 creative partner, producing 8,000+ original ads every month from a 100,000 sq. ft. Los Angeles studio, backed by a library of 1.6 million+ performance ads and $3B in annual managed ad spend. That is what gives TubeScience one of the richest first-party creative-performance datasets anywhere.

Labs turns that data — and the playbook behind billions in spend — into frontier AI tools that actually ship. We are an end-to-end native agentic AI lab from research, design, coding, experimentation to delivery. Our pipelines run against real creative, real deadlines, and real budgets every day.

You'll join a small, AI-empowered, senior team of engineers and product managers, with direct access to expert users, and you will own the automation and deployment infrastructure the rest of the team ships on. You’ll be expected to work across all frontier and open-weight models for every phase of your job from discovery to prototyping, to specs and building, and to evals and deployment.

The role

You'll build and run the automated infrastructure the team ships on: CI/CD pipelines, deployments, environments and the tooling that keeps releases fast and safe. The work is building automation, not manual bug fixing. You'll work with AI agents every day, and you remain accountable for every line that ships.

Small senior team. No layers between you and the problem. A direct line from your commits to the people using them, who will tell you immediately if it is wrong.

We weigh directly relevant experience heavily. This role needs hands-on experience automating infrastructure and deployments, and a career that has grown steadily in scope.

We are looking for full-stack engineers that ship code every week, feel comfortable in an AI-first environment and are open to remote, async cooperation with a LA-based team.

Minimum qualifications

• You have a bachelor's degree in a technical field. Computer science, engineering or a related discipline.
• Your software engineering career has progressed steadily. You can show how your scope and responsibility grew from role to role.
• You have built automated infrastructure. Infrastructure as code with tools such as Terraform or Pulumi, and environments you can rebuild from scratch.
• You have built and run CI/CD pipelines. Automated builds, tests and deployments with tools such as GitHub Actions, GitLab CI or Jenkins, including safe releases and rollbacks.
• You are a full-stack engineer. You have used Python and TypeScript in production, on both ends of the stack.
• You automate instead of patching by hand. When something breaks, you fix the process that let it break.
• Your English is at least upper intermediate or B2. 

Preferred Qualifications

• You have worked at an international company, with teammates across countries and time zones.
• You have used Docker, Kubernetes and a major cloud provider such as AWS or GCP.
• You use AI coding tools and agents in your daily work.

The problems to solve

• Robust media pipelines. Many assets a day through ingest, transform and render, on infrastructure that has to scale with them.
• Durable workflows. Long-running jobs that survive restarts, partial failure, and being changed while in flight.
• Model routing and spend. Several vendors, several models, permissions and cost tracked per team. Choosing the right model per job without a human in the loop, and knowing when the cheap one is good enough.
• Automated releases you can trust. Automated tests, staged rollouts and quick rollbacks, so a bad deploy is a short fix rather than a long night.
• Many projects, one system. Several products that share data, users and infrastructure, and only stay coherent if someone holds the architecture and the standards.
• Shipping in days. Going from a suggestion to production in days rather than quarters. That constrains architecture far more than it sounds like it does.

How the hiring works

Three conversations and a short piece of practical work. Recruiter screen, then an engineer from the team, then a practical assignment and a walkthrough with the hiring manager.

Roughly 17 business days end to end if we both move quickly. You will get a decision either way at every stage.