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Siemens Energy
AI Student Intern - Agentic AI for Power GridsSiemens Energy • Ontario, Oakville
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AI Student Intern - Agentic AI for Power Grids

AI Student Intern - Agentic AI for Power Grids

Siemens Energy • Ontario, Oakville
29 days ago
Job type
  • Full-time
  • Part-time
  • Remote
Job description

AI Student Intern - Agentic AI for Power Grids

About the Role

Location Canada Ontario Oakville Remote vs. Office Remote only Company Siemens Energy Canada Limited Organization Grid Technologies Business Unit Digital Grid Full / Part time Full-time Experience Level Student (Not Yet Graduated)

A Snapshot of Your Day

Join Siemens Energy new AI initiative to transform power grids. At our newly established AI Lab we are tackling the critical challenge of global grid congestion and revolutionizing antiquated grid operation and planning. We're combining 100 years of energy domain expertise with the latest advancements in AI foundation models for energy applications to fundamentally reshape the future of substations and power grids.

The AI Lab will be pioneering innovative and transformational solutions that will be productized and deployed across a large global footprint. This is your chance to drive a paradigm shift in the energy sector and build a more resilient and sustainable energy infrastructure

How You’ll Make an Impact

We are seeking a talented and high-velocity AI Intern to join our founding team to build the critical infrastructure for Agentic AI in the power sector. You will be hands-on, building the end-to-end loops where foundation models meet physical simulators. Working alongside research scientists, you will translate complex papers into high-performance code, rapidly iterating from "abstract idea" to "working agent".

Responsibilities:

  • Agentic Loops: Help design and implement the "closed-learning loops" where foundation models (LLMs, GNNs, Diffusion) interact with external simulation APIs. You’ll be building the reasoning and tool-use layers that allow agents to "think" and "act"
  • Rapid Prototyping: Act as the bridge between research and production. When a new paper on RLHF or long-horizon planning drops, you’re the one who has a working prototype running on our on-prem cluster by the end of the week.
  • Performance Tuning: Deep-dive into the training and RL stack. You’ll profile data loaders, optimize inference latency, and help squeeze every bit of performance out of our compute.
  • Environment Wrapping: Contribute to building "Gym-like" interfaces for simulators. You’ll help structure heterogeneous trajectory data (actions, environment responses, and reward signals) into training-ready pipelines.
  • MLOps : Integrate and manage the lifecycle of your experiments - tracking runs, managing checkpoints, and ensuring our training runs are reproducible and robust.
  • Documentation & Knowledge: Document your work ensuring maintainability and knowledge transfer within the lab

What You Bring

  • Technical Proactivity: A "high-agency" mindset. You take ownership of your work, identify technical gaps before they become issues, and are comfortable working across the entire stack; OSS contributions, winning hackathons, or publishing "just for fun" research is a HUGE PLUS.
  • PyTorch proficiency: You’ve tinkered with one of SFT, PEFT, and have a desire to master RL-post training and RLHF
  • Interest in Agentic Architectures: A demonstrated interest or project history in tool-use, function calling, or long-horizon planning within LLM architectures
  • Systems & Software Foundations: Proficiency with Docker, Linux environments, and REST API development. You should be comfortable managing the "plumbing" of AI, not just the modeling
  • Data Fluency: Ability to ingest and structure complex, heterogenous data (from technical documentation and code to simulation output files)
  • Currently pursuing or recently completed a degree in Computer Science, Engineering, Math, Physics, or a related field
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AI Student Intern - Agentic AI for Power Grids • Ontario, Oakville

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