AI/ML Software Engineer Intern β€” Hybrid in Cambridge

AI/ML Software Engineer Intern β€” Hybrid in Cambridge

Cambridge Full-Time 20000 - 30000 Β£ / year (est.) No working from home possible
Siemens

At a Glance

  • Tasks: Assist in developing AI strategies and collaborate on innovative projects.
  • Company: SIEMENS, a leader in technology with a supportive team culture.
  • Benefits: Paid internship, flexible hybrid work, and professional growth opportunities.
  • Other info: 12-month internship with a focus on nurturing your talents.
  • Why this job: Gain hands-on experience in AI/ML and contribute to exciting projects.
  • Qualifications: Passion for AI/ML and willingness to learn in a collaborative environment.

The predicted salary is between 20000 - 30000 Β£ per year.

SIEMENS in Cambridge is offering a 12-month paid internship focused on Artificial Intelligence and Machine Learning. As an intern, you will assist in developing AI strategies within the Teamcenter Structures applications, collaborate with system architects, and contribute to innovative projects. This position offers a friendly team environment where your talents will be nurtured, allowing you to grow in a hybrid work model that emphasizes flexibility and trust.

AI/ML Software Engineer Intern β€” Hybrid in Cambridge employer: Siemens

SIEMENS in Cambridge is an exceptional employer, providing a supportive and collaborative environment for interns to thrive in the fields of Artificial Intelligence and Machine Learning. With a strong emphasis on employee growth, this 12-month paid internship offers hands-on experience in innovative projects, while the hybrid work model ensures flexibility and a healthy work-life balance, making it an ideal place for aspiring engineers to develop their skills.

Siemens

Contact Details:

Siemens Recruitment Team

We think you need these skills to ace AI/ML Software Engineer Intern β€” Hybrid in Cambridge

Artificial Intelligence
Machine Learning
Collaboration
Software Development
Teamwork
Problem-Solving Skills
Adaptability