Scientific Researchers

Scientific Researchers — Multiple Positions

Open Scientific Domains | AI Training, Evaluation, Reinforcement Learning and Simulation

Axia is building a marketplace and research platform that connects specialized knowledge with AI model development. Our scientific research program turns domain expertise, experimental workflows and real-world data into evaluations, training datasets and environments for scientific AI agents.

We are looking for multiple researchers with deep expertise across scientific and technical disciplines to help shape this program.

How do we determine whether an AI model can conduct meaningful scientific reasoning, design a useful experiment or solve a problem beyond its training examples?

At Axia, you will help answer these questions within your area of expertise. You will identify valuable scientific workflows, establish what credible performance looks like, and develop the data, evaluations and environments that help models improve.

We are building a team of researchers with complementary specialties. You should bring depth in one or more domains; you are not expected to be an expert across all of them. Your research focus and responsibilities will reflect your expertise and the problems we identify together.

We welcome researchers with in-depth knowledge in areas including:

• Biology and life sciences: molecular and cellular biology, genetics, genomics, proteomics, systems biology, microbiology, neuroscience and bioinformatics.
• Chemistry: organic, inorganic, physical, analytical and computational chemistry; chemical synthesis, catalysis and molecular design.
• Physics: theoretical, experimental and computational physics; condensed matter, optics, photonics, quantum science and plasma physics.
• Materials science: materials discovery and characterization, polymers, metals, semiconductors, composites, nanomaterials and functional materials.
• Mathematics and statistics: applied and pure mathematics, probability, statistical inference, optimization, numerical analysis and formal reasoning.
• Engineering and robotics: mechanical, electrical, chemical, civil, aerospace and biomedical engineering; control systems, robotics and manufacturing.
• Medicine and pharmaceutical sciences: biomedical research, pharmacology, drug discovery, translational science, epidemiology and computational medicine.
• Climate, Earth and environmental sciences: atmospheric science, oceanography, geoscience, hydrology, ecology and environmental modeling.
• Energy and electrochemistry: batteries, energy storage, renewable energy, power systems, nuclear science, fusion and electrochemical systems.
• Agricultural and food sciences: plant science, crop research, soil science, agricultural systems and food science.
• Computational science and scientific machine learning: numerical simulation, high-performance computing, multiphysics modeling, inverse problems and AI for scientific discovery.

This list is open. We encourage applicants from other research-intensive disciplines and from fields that span several of these areas.

Depending on your expertise and role, you will:

• Identify meaningful scientific problems. Find gaps in model capabilities and existing benchmarks. Prioritize workflows based on scientific value, practical demand, data access, verifiability and experimental cost.
• Translate domain expertise into learning tasks. Capture hypotheses, protocols, observations, analysis code, instrument constraints, uncertainty and failed experiments. Develop tasks that require reasoning, tool use and iterative investigation.
• Build rigorous scientific evaluations. Establish reference standards through measured results, validated calculations, formal checks or structured expert review. Account for uncertainty and multiple scientifically defensible approaches.
• Develop training data and learning environments. Work with the AI research team on demonstrations, supervised fine-tuning data, reinforcement learning trajector