About Us:
Dispatch Energy is a fast-growing energy transition platform focused on distributed generation, grid resiliency, and innovation in clean energy deployment. As part of our broader mission, we are building a cutting-edge internal platform powered by a large language model (LLM) to augment workflows across the enterprise—from engineering and development to business operations.
Role Overview:
We’re looking for an Engineering Intern to join our small, high-impact team and contribute to the development of this internal LLM-powered platform. You’ll gain hands-on experience across the stack—from GPU-level optimization and model tuning to back-end infrastructure and front-end application development.
What You’ll Do:
- Work across the entire ML/AI stack supporting the LLM-powered platform.
- Assist with training, fine-tuning, and deploying models using open-source frameworks.
- Support data engineering and orchestration pipelines for LLM inference and retrieval.
- Collaborate on frontend features to integrate the LLM into real-world Dispatch workflows.
- Help build scalable, containerized services using tools like Docker and Kubernetes.
- Contribute to performance benchmarking, GPU optimization, and inference serving.
What We’re Looking For:
- Experience using at least 5 of the following technologies, languages, or frameworks:
- GitHub, Tensorflow, PyTorch, Pandas, NumPy, Django, Hugging Face, Perplexity, Docker, Cursor, SQL, RAGFlow, Pinecone, Spark, Apache Arrow.
- Strong fundamentals in software engineering and a passion for applied machine learning.
- Ability to work autonomously in a fast-paced startup environment.
- Curiosity, creativity, and a bias toward building.
- Preferred but not required:
- Experience in distributed learning research.
- Familiarity with power markets or energy infrastructure.
Compensation & Logistics:
- Pay: $50/hour
- Start Date: ASAP
- Location: Flexible — New York-based candidates preferred but remote applicants welcome.
How to Apply:
Please submit your resume and include links to any public GitHub repositories or code samples relevant to this work. Candidates without code samples will not be prioritized.
