ML Research Scientist

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.

We are hiring exceptional researchers and engineers across multiple areas of AI systems and next-generation 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. The applicant should be comfortable being hands-on, managing projects end-to-end, moving fluently between mathematical formulation and empirical validation, and turning research ideas into clean, well-tested code. You will work directly with the founding team to research, develop, and validate model-optimization techniques, and help align that work with the broader platform.

This is a hands-on, research-meets-engineering role at the intersection of modern ML and next-generation AI compute. Researchers excited by deep learning, model efficiency, and fast-paced early‑stage environments are encouraged to apply.

• 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.
• Collaborate with the team to align algorithmic work with the broader platform.

• 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.
• Comfortable owning and driving projects independently, and setting technical direction under uncertainty.
• Strong written and verbal communication.
• Deep prior experience with deep learning and LLMs is valued, but a strong mathematical foundation and the drive to apply it to modern models matters more. If your background is in applied mathematics, statistics, physics, or scientific computing rather than mainstream deep learning, we still encourage you to apply.
• Background in probabilistic or Bayesian machine learning.
• Experience with Monte Carlo methods, stochastic approximation, or uncertainty quantification.
• Some experience with C++ or performance-oriented programming.
• Familiarity with model compression, quantization, pruning, or low-precision inference.
• Publications or open-source work in deep learning, applied ML, or a related area.

• Hands‑on work making state-of-the-art AI models dramatically leaner and more efficient.
• The chance to take principled methods from mathematics all the way to measured, real-world impact.
• A foundational role at an early‑stage company, working directly with the founding team.
• Deep collaboration with a team building next‑generation AI compute through hardware‑software co‑design and ML.

Please reach out to  for any questions.