DevOps Engineering Intern

Job Details:

Job Description: 

Join the DevOpsAI Integration and Distribution organization and gain hands-on experience operating the continuous integration infrastructure behind large-scale driver and platform validation.

Our CI systems run validation around the clock on a large fleet of physical test machines. Today the bulk of that is Linux graphics driver testing, and the scope is expanding to other platforms and products. At this scale, hardware failures are a daily occurrence, and they surface as reports from developers whose tests failed on a machine they suspect is faulty.

As an intern on this team, you will be the first line of triage for those reports. You will confirm what actually happened, determine whether the fault lies in the machine or in the software under test, and route it to the right owner - a lab support request for a physical repair, or back to the development team when a test left the system in a state that only looks like a hardware fault. Distinguishing between the two is the core skill of this role and the one you will develop fastest.

The work is primarily analytical and log-driven: reading system logs, debug output, and monitoring dashboards, and recognizing the detail that does not fit. We value curiosity highly here - the instinct to keep investigating until the cause is understood - and we care more about how you approach a problem than about the length of your CV.

There is also scope to apply AI in this role, and we encourage it: helpers that summarize or classify large volumes of log data, and monitoring tools that use AI to surface anomalies worth a human review. This is both a practical way to accelerate your own work and a direction that matters to the wider organization.

The team and how we work

The team is based in Poland and works in a hybrid model. Because a significant part of this work involves physical test systems in the lab, regular on-site presence is required; this is not a fully remote position. We are a small, fast-moving team with an informal and collaborative culture.

Key Responsibilities

- Triage incoming infrastructure failure reports: review logs and test artifacts, assess the state of the machine, and determine whether the fault originates in the hardware or in the software under test.
- Raise lab support requests for confirmed hardware faults, documenting the affected component, the symptoms observed, and the causes already ruled out.
- Return software-induced failures to the owning team, identifying the test or change that left the system in a faulty state.
- Monitor the health of the test fleet across our dashboards - machine availability, device detection, storage and network errors - and escalate anomalies before they lead to widespread test failures.
- Collect diagnostics on demand: system and kernel logs, console output, device and firmware state, and artifacts from specific CI runs.
- Work directly with test systems in the lab: verify connections and peripherals, confirm component replacements, and validate that a machine is ready to return to production.
- Track recurring failures so the team can identify the least reliable components and platforms, and feed those findings into how the environment is built.
- Automate recurring checks, including with AI-assisted tooling, turning manual dashboard reviews and log analysis into repeatable processes.

Qualifications:

Minimum Qualifications

The candidate must be pursuing a Bachelor's degree in Computer Science, Computer Engineering, Electronics, Electrical Engineering, or a related field.
- Curiosity and persistence in investigating technical problems, with the patience to work through logs and evidence.
- Working knowledge of the Linux command line and the ability to read system and kernel logs.
- Basic scripting ability in Python or shell.
- A methodical, evidence-based approach to troubleshooting, including the ability to state clearly what has been ruled out.
- Clear written communication in English, as much of this work involves handing findings to others in an actionable form.
- Availability to work on site in the lab on a regular basis, within a hybrid arrangement.

Preferred Qualifications
- Exposure to CI/CD systems, monitoring, or observability tooling.
- Interest in applying AI and large language models to practical problems such as log analysis, failure classification, or anomaly detection.
- Practical PC hardware experience, including assembling systems, replacing components, and diagnosing faults.
- Familiarity with containers and automation tooling such as Docker, Jenkins, or Ansible.
- Experience with log or data analysis and with presenting findings clearly.
- Familiarity with Git and pull request workflows.
- Interest in open source and in the operation of large-scale validation infrastructure.

Job Type:

Student / Intern

Shift:

Shift 1 (Poland)

Primary Location: 

Poland, Gdansk

Additional Locations:

Posting Statement:

All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.

Position of Trust

N/A

EU Salary

We offer a total compensation package that ranks among the best in the industry.

The minimum Annual Salary for this job which could be performed in Poland is: 68 880,00 zł The job posting reflects the minimum base compensation for this role. Our standard internship rates are aligned with the specific job role and degree level. As part of our total compensation approach, the final offer may also include additional pay components and benefits. Your recruiting team will provide the confirmed base salary along with any applicable total compensation elements upon employment offer delivery.

Work Model for this Role

This role will require an on-site presence. * Job posting details (such as work model, location or time type) are subject to change.

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