Machine Learning Research Intern
Ludwig Computing · San Francisco, CA
Work directly with the founding team to research, implement and validate techniques that make large language models smaller, faster and more efficient. Also build software infrastructure for rapid experimentation. The internship may lead to a full-time role.
What you'll do
- Research and implement LLM compression and optimization techniques while preserving accuracy
- Train and fine-tune models in PyTorch, occasionally C++
- Build infrastructure to prototype, run, track and reproduce experiments
- Distill recent literature into concrete experiments and report findings
- Explore how efficiency relates to reliability and reasoning quality
Requirements
- Strong Python programming skills
- Hands-on experience with PyTorch or a comparable deep-learning framework
- Solid grasp of machine learning and deep learning fundamentals
- Experience training or fine-tuning neural networks and evaluating with standard metrics
- Comfortable reading research papers and turning them into code
- Currently pursuing a degree in Computer Science, Electrical Engineering, Machine Learning, or a related field
Skills
Outlined skills are listed as required.
Full original description
At Ludwig Computing, we are solving the energy efficiency problem of intelligent compute. Our novel co-designed approach is optimized to deliver radical improvements in energy efficiency and performance across a wide range of AI workloads. Join us at the ground floor as we build the future of intelligent compute.
We are looking for technically exceptional and intellectually curious machine learning interns excited about making large language models smaller, faster, and more efficient. You will work directly with the founding team to research, implement, and validate model-optimization techniques, while building software infrastructure that enables rapid experimentation and iteration. The role is designed with a potential path toward a full-time position for high-performing candidates.
Responsibilities
- Research and implement techniques to compress and optimize large language models while preserving accuracy.
- Train and fine-tune models using PyTorch (and occasionally C++), measuring results against standard quality metrics.
- Build and maintain software infrastructure to rapidly prototype, run, track, and reproduce experiments.
- Read recent literature, distill it into concrete experiments, and report findings clearly.
- Explore how model efficiency connects to model reliability and reasoning quality.
Requirements
- Strong programming skills in Python, with hands-on experience in PyTorch (or a comparable deep-learning framework).
- Solid grasp of machine learning and deep learning fundamentals.
- Experience training or fine-tuning neural networks and evaluating them with standard metrics.
- Comfortable reading research papers and turning them into working code.
- Currently pursuing a degree in Computer Science, Electrical Engineering, Machine Learning, or a related field.