ML Research Scientist
Ludwig Computing · San Francisco, CA
Work directly with the founding team to research, develop and validate techniques that make large AI models smaller, faster and more efficient. Move between mathematical formulation and empirical validation, building prototypes and running rigorous experiments on real models.
What you'll do
- Research and validate techniques to compress and accelerate large AI models while preserving accuracy
- Move between mathematical formulation and empirical validation of novel methods
- Build prototypes in PyTorch and tooling for tracking and reproducing experiments
- Establish baselines and design careful experiments and ablations
- Read recent literature and turn it into concrete experiments
Requirements
- PhD or equivalent research experience in computer science, electrical engineering, machine learning, applied mathematics, statistics, information theory, physics, or a related field
- Strong command of probability, statistics, optimization, and linear algebra
- Strong Python programming skills with hands-on PyTorch (or comparable framework) experience
- Ability to develop original ideas and turn them into well-designed computational experiments
- Sound experimental judgment: baselines, ablation design, reproducibility, honest treatment of negative results
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 a technically exceptional and intellectually curious ML research scientist excited about making large AI models smaller, faster, and more efficient. You will work directly with the founding team to research, develop, and validate model-optimization techniques, moving fluently between mathematical formulation and empirical validation, and help align that work with the broader platform.
Responsibilities
- Research, develop, and validate techniques to compress, optimize, and accelerate large AI models while preserving accuracy.
- Move fluently between mathematical formulation and empirical validation — take a novel method, characterize its behavior and cost, and test it on real models.
- Build high-quality prototypes in PyTorch (or a comparable framework) and the tooling needed to run, track, and reproduce experiments.
- Establish rigorous baselines and design careful experiments and ablations that separate genuine gains from artifacts.
- Read recent literature, distill it into concrete experiments, and report findings clearly.
Requirements
- PhD or equivalent research experience in computer science, electrical engineering, machine learning, applied mathematics, statistics, information theory, physics, or a related field.
- Strong command of probability, statistics, optimization, and linear algebra.
- Strong programming skills in Python, with hands-on experience in PyTorch (or a comparable deep-learning framework).
- Ability to develop original ideas and turn them into well-designed computational experiments.
- Sound experimental judgment: careful baselines, ablation design, reproducibility, and honest treatment of negative results.