ML Research Intern - Model Efficiency

Job type: Internship
Location: San Fransisco Bay Area (on-site or remote)
Compensation: 30-50 / hour depending on experience and qualifications

About us
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. We are building a future where high-performance computing is powered by leaner, smarter, and extremely efficient hardware and software platforms. Join us at the ground floor as we build the future of intelligent compute.

Role Description
We are hiring exceptional engineers and researchers across multiple areas of AI systems and next-generation 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.
This is a hands-on, research-meets-engineering  role at the intersection of modern ML and next-generation AI compute. Candidates interested in deep learning, model efficiency, and fast-paced startup environments are encouraged to apply. 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.
•    Collaborate with the team to align algorithmic work with the broader platform.

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.
•    Ability to write clean, organized code and build small tools and pipelines for experimentation.
•    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.

Bonus if you have
•    Experience working with large language models (training, fine-tuning, evaluation, reasoning).
•    Familiarity with model compression, quantization, or pruning.
•    Familiarity with probabilistic ML models.
•    Experience with experiment tracking and ML tooling (e.g., Weights & Biases, MLflow, or similar).
•    Some experience with C++ or performance-oriented programming.
•    Coursework, projects, or publications in deep learning, NLP, or applied ML.
•    Currently pursuing or recently completed a PhD or MS in CS, EE, ML, or related field.

What you'll Gain
•    Hands-on experience making state-of-the-art language models leaner and more efficient.
•    Practical skills in LLM training and inference, optimization, and large-scale experimentation.
•    Mentorship from a team building next-generation AI compute through deep expertise in hardware-software co-design and ML.