Senior Infrastructure Engineer

**Description**:
Are you tired of your good old corporate job, working on one project for half a year? Is your passion for making great infrastructure being kept in a box labeled “for later”? Do you enjoy working with smart people and do you want to make their developer experience great?

Zendar is looking for an experienced Infrastructure Engineer who understands engineering needs and can work with engineers to make their life easier and more productive. Our team is a testament of the ability to unite engineers of different backgrounds over a common vision. We do not shy away from reality and its limitations but we also dream big.

You don’t have to fit in a box. We are not looking for a superhero. But we might be looking for you

**Your Role**:
To support our growing team, we’re looking for an** Infrastructure Engineer** to elevate our processes and technologies to the next level. If you’re excited about all infrastructure topics, starting at IT administration, going through the CI/CD processes, and ending in designing and managing the ML data lifecycle, we’d love to hear from you

You will join our small team of Infrastructure engineers located in Berkeley, California and Paris, which means that not only will you have a lot of influence in shaping all of IT/DevOps/MLOps at Zendar, but you will also have a lot of responsibility for juggling many priorities and support tickets. This role is perfect for someone who is comfortable managing multiple responsibilities simultaneously, enjoys interacting frequently with other engineers, and is opinionated about what to prioritize and how to build things.

Key Responsibilities
- **IT Management of European offices (Paris and Lindau, Germany)**
- Support engineers in their everyday needs - user management, permission control, system configuration.
- **CI/CD and Infrastructure**:

- Maintain and adapt CI pipelines using **GitHub Actions**, ensuring they remain efficient and scalable as we grow.
- ML Data & Workflow management:

- Maintain and grow a robust and reliable data lake in GCP for our proprietary training and evaluation data.
- Take ownership of a high-capacity batch job system (Apache Airflow) for compute workloads.
- **Setting and Maintaining Infrastructure Standards**:

- Be a go-to resource for solving infrastructure-related challenges, fostering a culture of ownership and accountability in the team.

**What We Look For**:

- Ability to work from the office in Paris at least on Monday, Tuesday, and Thursday. Wednesday and Friday are optional in-office days.
- 5+ years in Infrastructure engineering, DevOps/MLOps or similar
- Hands-on experience with CI/CD tools like **GitHub Actions** and a solid understanding of deployment workflows.
- Knowledge of Terraform and Ansible.
- Experience with cloud computing platforms (**GCP** or other).
- Broad knowledge of data engineering and MLOps tools (Airflow, Kubernetes).
- Proficiency with Python and Bash.
- A proactive approach to solving problems and willingness to take on new challenges as they arise.
- Somebody with solid opinions based on experience, yet open-minded and flexible to find the best solution for the situation.

Bonus Points
- Prior experience of supporting ML research or data collection & management.
- Hand-on experience of driving the transformation from on-prem to in-cloud environment.
- Familiarity with build orchestration technologies and dependency management tools, comfort with containerization (**Docker**).
- A track record of excelling in ambiguous or evolving roles.
- Our current CI runs in several CI runners and as a result, some failures are difficult to reproduce and debug. We are looking for a more containerized approach to CI, resulting in better debuggability.
- We need to improve the release process for several of our repositories. For our monorepo that means revisiting our testing strategy and making sure we can get a high-quality signal on the state of our release branch in a short period of time. For our smaller repos, we need to set up the release processes from scratch.
- Having large datasets at three geographical sites results in different challenges and some synchronization between locations is inevitable. We are currently working with different NAS devices and localized cloud buckets but we are ready to upgrade our data management capabilities.
- Training ML models on data from our test vehicles benefits greatly from creating content-based subsets. Automating data labeling is the logical next step, significantly reducing manual effort and streamlining the data pipeline.
- Our ML team uses the raw data to train their models. This consists of several steps, generating different versions of processed data. It would be beneficial to unify different ML workflows and enable storing these processed data to speed up the processing times.

**What We Offer**:

- Opportunity to make an impact at a young, venture-backed company in an emerging market
- Collaboration with smart and mo