Drivetrain

Drivetrain

Engineering Intern – Gen AI for FP&A Platform

United States · Intern · Internship

Sponsorship not specifiedDetected 409 days ago
Data StructuresAlgorithmsCloud PlatformsMachine LearningLLMsRAGAgentic AIFP&ACommunicationProblem SolvingMentoring

About the role

  • We are seeking highly motivated Computer Science engineering interns passionate about Generative AI to join our team.
  • You will work on real-world projects involving Retrieval-Augmented Generation (RAG), Agentic AI, and Large Language Models (LLMs) to enhance our FP&A (Financial Planning & Analysis) platform.
  • This is a unique opportunity to gain hands-on experience at the intersection of AI and enterprise automation.

Responsibilities

  • Develop & Experiment: Build and prototype Gen AI solutions using RAG, agentic workflows, and LLMs for FP&A use cases.
  • Collaborate: Work closely with product and engineering teams to integrate AI-driven features into the platform.
  • Optimize: Apply strong computer science fundamentals to design efficient algorithms, data structures, and scalable systems.
  • Document & Present: Clearly document your work, build workflow diagrams, and present results to the team.
  • Build and prototype Gen AI solutions using RAG, agentic workflows, and LLMs for FP&A use cases.
  • Apply strong computer science fundamentals to design efficient algorithms, data structures, and scalable systems.
  • Clearly document your work, build workflow diagrams, and present results to the team.
  • Learn from industry experts and collaborate with a passionate team.

Requirements

  • Drivetrain brings together the best and the brightest, no matter where they are and provides them a great degree of autonomy.
  • Currently pursuing or recently completed a degree in Computer Science or a related field.
  • Academic Background: Currently pursuing or recently completed a degree in Computer Science or a related field.

Nice to have

  • Familiarity with concepts such as RAG, Agentic AI, and LLMs.
  • Evidence of completed projects involving Gen AI, RAG, or agentic workflows.

Skills

  • AI/ML Exposure: Familiarity with concepts such as RAG, Agentic AI, and LLMs.
  • Completion of relevant projects is preferred.
  • Communication: Excellent verbal and written communication skills.
  • Preferred Project Portfolio: Evidence of completed projects involving Gen AI, RAG, or agentic workflows.
  • Experience with modern AI frameworks, cloud platforms, and API integration.
  • Why Drivetrain.ai?
  • Real-World Impact: Work on cutting-edge Gen AI projects for enterprise automation.
  • Flexibility: Remote or hybrid options available to suit your schedule.
  • Apply now and help shape the future of intelligent FP&A!
  • Candidates with a strong project portfolio and solid computer science fundamentals are encouraged to apply.
  • Sounds exciting?
  • Apply at careers@drivetrain.ai.

Benefits

  • We follow a product-led growth strategy, continuously learning from our customers and collaborating to build the amazing software that Drivetrain is.

Company info

  • Drivetrain is on a mission to empower businesses to make better decisions.
  • Our financial planning & decision-making platform helps companies scale and achieve their targets predictably.
  • Drivetrain is a remote-first company headquartered in the San Francisco Bay Area.
  • Founded in 2021 by a couple of ex-Googlers, Drivetrain is a fast-growing company on a trajectory for success with backing from leading venture capital firms.
  • Drivetrain provides a great culture for its employees to thrive in and be happy. 💜
  • We provide an environment to explore new ideas, to take risks, to make mistakes, and to learn, so you can succeed.
  • Anyone in the company can come up with great ideas and become a catalyst for positive change.
  • We let the best ideas win. 👥
  • About the Role We are seeking highly motivated Computer Science engineering interns passionate about Generative AI to join our team.

This listing is sourced directly from Drivetrain's careers page and normalized into a canonical job model.