RMCC Systems Automation Intern

Bloom Energy Corporation

San Jose (CA)

On-site

USD 50,000 - 69,000

Full time

4 days ago
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Job summary

Bloom Energy in San Jose, CA is seeking a Systems Engineering Intern for a summer, on-site internship starting May/June. The intern will work on RMCC operations, develop Python scripts to automate monitoring and troubleshooting, and collaborate with engineering teams to deliver automation solutions that improve efficiency and reliability.

The role emphasizes hands-on fuel-cell operations familiarity, analytics, and a willingness to learn complex systems.

Qualifications

  • Pursuing a Bachelor's or Master's degree in a technical field.
  • ],
  • job_responsibilities_description':['Learn Bloom Energy fuel cell operation, monitoring tools, and RMCC workflows.','Support daily fleet monitoring, alarm analysis, troubleshooting, health checks and operational activities.','Develop Python-based scripts to automate repetitive workflows.','Translate operator knowledge into scalable automation solutions.','Test, document, and deploy automation solutions to improve efficiency and response time.','Collaborate with RMCC, Field Service, Product Support, and Engineering teams on automation initiatives.'],
  • CoT_job_summary_short
  • COMPANY NAME: Bloom EnergyKEY POINTS: Python automation, RMCC workflow, internship in on-site San Jose
  • job_summary_short
  • <p>Bloom Energy is seeking a Systems Engineering Intern for a summer on-site role in San Jose. The internship starts in May/June and runs through the summer, with an end date in December 11, 2026. You will learn fuel-cell operations, RMCC workflows, and collaborate with cross-functional teams to deliver automation solutions that improve operational efficiency and response time.</p><p>The role emphasizes hands-on development of Python-based scripts, troubleshooting, and documenting automated tools, while building foundational experience in energy systems and automation.</p>
  • contract_type
  • fulltime
  • location_type
  • on site
  • remote_scope
  • null
  • perks
  • []
  • stated_min
  • 36
  • stated_max
  • 50
  • stated_absolute
  • null
  • salary_estimation_cot
  • Internship in San Jose, CA: $36-$50/hr, on-site, May/June start, hourly pay
  • estimated_low
  • 36
  • estimated_medium
  • 43
  • estimated_high
  • 50
  • currency
  • USD
  • frequency
  • hourly
  • bonus
  • null
  • tax
  • gross
  • application_email
  • null
  • job_end_date
  • 2026-12-11
  • contact_person
  • null
  • hiring_department_name
  • null

Responsibilities

  • Learn Bloom Energy fuel cell operation, monitoring tools, and RMCC operational workflows.
  • Support daily fleet monitoring, alarm analysis, troubleshooting, system health checks, and operational activities.
  • Work with RMCC operators and engineers to understand existing procedures and identify opportunities for process improvement.
  • Develop Python-based scripts and tools to automate repetitive operational, troubleshooting, data-analysis, and reporting workflows.
  • Translate operator knowledge and procedures into scalable, repeatable automated solutions.
  • Test, document, and deploy automation solutions that improve operator efficiency, consistency, and response time.
  • Collaborate with RMCC, Field Service, Product Support, Controls, and Engineering teams on operational and automation initiatives.

Skills

Python programming
Analytical thinking
Teamwork
Automation interest

Education

Bachelor's or Master's degree in Mechanical/Electrical/Chemical/CS/Software/Data Science or related

Tools

Python
SQL/NoSQL
REST APIs
Git
Data visualization

Job description

Bloom Energy in San Jose, CA is seeking a Systems Engineering Intern for a summer, on-site internship starting May/June. The intern will work on RMCC operations, develop Python scripts to automate monitoring and troubleshooting, and collaborate with engineering teams to deliver automation solutions that improve efficiency and reliability.

The role emphasizes hands-on fuel-cell operations familiarity, analytics, and a willingness to learn complex systems.

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