Job Description Summary

GE Vernova is embracing cutting-edge technologies to streamline operations, improve customer experiences, and drive growth. As part of this vision, we are building a dedicated team to leverage Generative AI and redefine how we engage with our industry. The ultimate goal of the team is to automate prompting, develop a framework for prompting across use cases to maximize reuse as well as sharing across businesses.

We are seeking a highly motivated and talented Prompt Engineer to join our dynamic team. The successful candidate will be responsible for creating and optimizing prompts for LLMs, collaborating with domain experts and end users to develop software solutions that address both internal and external needs. This position offers a great opportunity to work at the intersection of advanced technology and traditional industry, making a tangible impact on our business processes.

As a GE Vernova accelerator, GE Vernova Advanced Research is driving strategy and leading research & development efforts to execute on the business’s mission to help power the energy transition. We forge the collaborations and help invent the technologies required to electrify and decarbonize for a zero-carbon future.
Representing virtually every major scientific and engineering discipline, our researchers are collaborating with GE Vernova’s businesses, the U.S. government, and more than 420 entities at the forefront of technology to execute on 150+ energy[1]focused projects. Collectively, these research programs and initiatives aim to solve near term technical challenges, deliver next generation product advances, and drive long term breakthrough innovation to enable more affordable, reliable, sustainable, and secure energy.

Job Description

Responsibilities:

• Collaborate with domain experts to understand business needs and translate them into clear and effective LLM prompts.

• Develop, test, and refine prompts for a variety of use cases, ensuring high accuracy and relevance in generated outputs.

• Work closely with software developers to integrate LLM solutions into existing or new applications.

• Contribute to the development of a scalable framework for prompt engineering, promoting reuse and knowledge sharing across different business units.

• Assist in gathering and analyzing user feedback to continually improve prompt effectiveness.

• Stay up-to-date with advancements in LLM technology and contribute to strategic decisions on tool and platform selection.

• Document prompt engineering processes, best practices, and use case outcomes to build a comprehensive knowledge base.

Requirements:

• 5+ years of relevant experience and a Master’s or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, AI Engineering, or related fields.

• 3+ years of experience with prompt engineering and familiarity with tools such as OpenAI’s GPT models, Hugging Face, etc.

• 3+ years of experience with Python or other programming languages commonly used in AI/ML development.

• Strong problem-solving skills and attention to detail.

• Excellent communication skills, with the ability to articulate complex technical concepts to non-technical stakeholders.

• Ability to work collaboratively in a team environment and adapt to changing project requirements.

Eligibility:

• Must be willing to work out of an office located in Niskayuna, NY or Cambridge, MA.

• Accelerated Immersion for Senior talent: Impact starts with a deep understanding of our core capabilities. For Cambridge, MA based resources, this position includes a required three-month (hybrid) onboarding residency at the GE Vernova Advanced Research Center in Niskayuna, NY. This immersive program allows our senior talent to establish a strong technical foundation, collaborate with our premier scientists and engineers, and align with our mission to lead the energy transition from the epicenter of our innovation hub. Travel, housing, and living stipend provided.

Ideal Candidate Characteristics:

• Master’s or PhD degree in Computer Science, Engineering, Data Science, or related field

• Experience with machine learning frameworks (e.g., PyTorch) and data processing libraries (e.g., Pandas, NumPy).

• Previous experience or internships involving NLP or machine learning projects.

• Familiarity with version control systems like Git.

• Understanding of traditional industry operations and challenges.

 

 

For candidates applying to a U.S. based position, the pay range for this position is between $113,200.00 and $188,800.00. The Company pays a geographic differential of 110%, 120% or 130% of salary in certain areas. The specific pay offered may be influenced by a variety of factors, including the candidate’s experience, education, and skill set.

 

 

Bonus eligibility: discretionary annual bonus.

 

 

This posting is expected to remain open for at least seven days after it was posted on April 27, 2026.

 

 

Available benefits include medical, dental, vision, and prescription drug coverage; access to Health Coach from GE Vernova, a 24/7 nurse-based resource; and access to the Employee Assistance Program, providing 24/7 confidential assessment, counseling and referral services. Retirement benefits include the GE Vernova Retirement Savings Plan, a tax-advantaged 401(k) savings opportunity with company matching contributions and company retirement contributions, as well as access to Fidelity resources and financial planning consultants. Other benefits include tuition assistance, adoption assistance, paid parental leave, disability benefits, life insurance, 12 paid holidays, and permissive time off.

 

 

GE Vernova Inc. or its affiliates (collectively or individually, “GE Vernova”) sponsor certain employee benefit plans or programs GE Vernova reserves the right to terminate, amend, suspend, replace, or modify its benefit plans and programs at any time and for any reason, in its sole discretion. No individual has a vested right to any benefit under a GE Vernova welfare benefit plan or program. This document does not create a contract of employment with any individual.