Menlo Institute
Job: AI Engineering Internship at a Non-Profit
Duration: Full-time, June – August (flexible)
Location: Menlo Park, California
How to Apply
Please submit your resume, a brief cover letter, and any relevant project portfolio or GitHub links to [email protected]. Applications will be reviewed on a rolling basis.
Company Overview
The Menlo Institute is a non-profit organization focused on democratizing artificial intelligence (AI) through available compute resources. We partner with leading academic institutions with the goal of matching GPU compute with researchers lacking sufficient funding.
Position Overview
We are seeking a motivated AI Engineering Intern to join our team and contribute to GPU compute resource engineering.
As an AI Engineering Intern, you will play a critical role in the GPU compute lifecycle. You will contribute to onboarding, managing, and offboarding open-source large language models (LLMs) from the GPU providers. You will be responsible with loading, training/fine-tuning, and inferencing the LLMs. You will also conduct performance benchmarks and evaluate GPU providers to identify their relative strengths and weaknesses. This internship offers hands-on experience in AI infrastructure, model optimization, and GPU cluster orchestration, providing an opportunity to work on real-world AI challenges.
Key Responsibilities
LLM Management
- Load and configure open-source large language models (LLMs) on GPU infrastructure.
- Fine-tune LLMs to meet specific performance or application requirements.
- Perform inference tasks and optimize model performance.
- Conduct benchmarks to evaluate model performance.
Provider Management
- Interface with various GPU providers to assess their services, pricing, and technical capabilities.
- Onboard new GPU providers, ensuring seamless integration into existing workflows.
- Manage ongoing model operations with GPU providers, including orchestration and troubleshooting.
- Offboard GPU providers as needed, ensuring proper documentation and transition.
Qualifications & Background
- Current enrollment, or within 2 years of graduation, of a Bachelors or Masters degree program in Computer Science, Data Science, Electrical Engineering, Mathematics, or a related field.
- Coursework or projects in machine learning, artificial intelligence, or cloud computing is a plus.
- Familiarity with relevant AI/ML libraries (e.g., PyTorch, TensorFlow, Hugging Face).
- Familiarity with orchestration (managed and manual Slurm/Kubernetes)
- Understanding of large language models (LLMs) and their training/inference processes.
- Experience with Linux-based environments and command-line tools.
- Exposure to benchmarking tools or performance evaluation techniques is desirable.
- Knowledge of cloud computing platforms (e.g., AWS, GCP, Azure) or GPU-based infrastructure is a plus.
- Prior internships, academic projects, or personal projects involving machine learning, AI model training, or cloud infrastructure are highly desirable.
- Experiences with GPU-based computing or managing cloud resources are highly desirable.
- Strong analytical and problem-solving skills with attention to detail.
- Excellent communication skills, both written and verbal.
- Ability to work independently and collaboratively in a fast-paced, dynamic environment.
- Curiosity and eagerness to learn about AI infrastructure and emerging technologies.
- Proactive and self-motivated with a willingness to take ownership of tasks.
- Passion for AI and interest in contributing to the optimization of AI infrastructure.
- Strong organizational skills to manage multiple tasks and document findings effectively.