Introduction
We value candidates who are motivated by building the mathematical and computational foundations for the next generation of intelligent systems and scientific modeling. Beyond technical ability, we look for people who resonate with our core philosophy:
Develop next-generation microscopic modeling and learning systems.
Focus Micro, Forecast Macro.
We are looking for candidates who enjoy working at the intersection of mathematics, algorithms, and machine learning systems. Ideal applicants are comfortable reasoning rigorously about mathematical structures and translating those ideas into reliable computational workflows.
Preferred Alignments
• Strong background in mathematics, statistics, theoretical computer science, physics, or a related field.
• Familiarity with linear algebra, probability theory, statistical learning theory, optimization, or neural networks.
• Experience thinking about algorithm correctness, generalization, or mathematically structured codebases.
• Comfort working with numerical computing, machine learning frameworks, or structured programming paradigms.
• Interest in learning theory, model architectures, or mathematically grounded machine learning systems.
• Ability to read and reason about technical research papers and translate them into implementable algorithms.
• Curiosity about theoretical foundations of deep learning and large-scale learning systems.
Nice-to-have:
• Experience with scientific computing, machine learning frameworks, or neural network research.
• Exposure to optimization theory, statistical modeling, or large-scale learning systems.
• Background in numerical methods, probabilistic modeling, or algorithmic analysis.
What You Will Be Doing:
As a Math Engineer (AI / ML), you will work on building mathematically grounded foundations for machine learning models and algorithmic systems.
Your work will include:
• Developing mathematical models for learning systems, neural architectures, or statistical inference processes.
• Translating theoretical learning algorithms and optimization procedures into structured computational implementations.
• Analyzing generalization properties, convergence behavior, and stability of learning algorithms.
• Collaborating with researchers to turn theoretical insights into reliable machine learning systems.
• Designing mathematically sound abstractions for models, optimization procedures, and learning dynamics.
• Building internal tooling and frameworks that support reproducible and reliable ML experimentation.
• Studying edge cases and theoretical properties such as training stability, optimization dynamics, and model robustness.
• Contributing to a research-oriented engineering environment where mathematical rigor meets practical machine learning systems.
The role sits between mathematical research and machine learning engineering, focusing on building infrastructure and theoretical foundations that make complex learning systems both understandable and reliable.
Why Us
Computation group plays the core role of Forecaster AI, we provide support to theoretical natural science research platform, develop novel computational approaches for quantum science, artificial intelligence and are gradually applying to physics, chemistry, finance and so on. We initiated the philosophy of focus micro, FORECAST MACRO, and keep practicing public awareness, openness, and respect.
We work rigorously, dream unlimited, and welcome peers to join.
We can consider housing coverage and OPT/H1B sponsorship based on availability.