Machine Learning Operations Engineer (m/f/d) at STARK Systems
Berlin, , Germany -
Full Time


Start Date

Immediate

Expiry Date

20 Nov, 25

Salary

0.0

Posted On

21 Aug, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Cloud, Continuous Improvement

Industry

Information Technology/IT

Description

ABOUT US

STARK is a new kind of defence technology company revolutionizing the way autonomous systems are deployed across multiple domains. We design, develop and manufacture high performance unmanned systems that are software-defined, mass-scalable, and cost effective. This provides our operators with a decisive edge in highly contested environments.
We’re focused on delivering deployable, high-performance systems—not future promises. In a time of rising threats, STARK is bolstering the technological edge of NATO Allies and their Partners to deter aggression and defend Europe—today.

QUALIFICATIONS

  • Bachelor’s or Master’s degree in Engineering or a related field.
  • MLOps tools such as DVC and Clear ML
  • CI/CD and cloud knowledge
  • Some Computer Vision experience or knowledge
  • Strong organisational skills
  • Excellent problem-solving skills and the ability to troubleshoot and resolve issues.
  • Strong communication and collaboration skills to work effectively in a multidisciplinary team environment.
  • Adaptability to work in a fast-paced, dynamic startup environment with a strong drive for innovation and continuous improvement.
  • Ability to travel as needed.

How To Apply:

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Responsibilities
  • Design, implement, and maintain data pipelines for processing large volumes of data, ensuring efficient data flow for model training, testing, and inference.
  • Build and manage automated Continuous Integration and Continuous Deployment (CI/CD) pipelines for model training, deployment, and monitoring, enabling seamless transitions from development to production.
  • Work closely with data scientists, software engineers, and IT teams to ensure smooth integration of machine learning models and data pipelines into production systems.
  • Stay current with emerging technologies and industry trends, recommending and implementing innovations to improve our products and processes
  • Dive deep into the details to identify, understand, and solve difficult technical problems.
  • Constantly be challenging requirements to determine what adds value and what is not.
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