regular labis an artificial intelligence and physical sciences company focused on accelerating scientific discovery. researcher, data position. This role is for experienced researchers and engineers interested in developing advanced AI systems to support breakthroughs in materials science, energy research, and other scientific fields.
The successful candidate will join Periodic Labs’ research team and contribute to the development of cutting-edge scientific AI models by creating high-quality datasets, designing evaluation systems, and building infrastructure that enables continuous improvement of machine learning models.
This position is available as a full-time opportunity based on Menlo Park, California, USA or Montreal, Canadawith plans to expand to San Francisco in the future.
About regular labs
Periodic Labs is building the next generation of artificial intelligence technology aimed at transforming the way scientific research is conducted. The company is focused on combining AI capabilities with physical science to accelerate discoveries that can shape the future of materials, energy, and advanced technologies.
The organization is at the forefront of scientific AI research, bringing together experts in machine learning, computational science, experimental research, and engineering. Its mission is centered on developing AI systems that support complex scientific workflows and enable researchers to solve difficult problems more efficiently.
Periodic Labs provides an environment where researchers can lead impactful projects and contribute to cutting-edge scientific advances, backed by leading investors and a rapidly growing team of experts.
Research Scientist, Data Role Overview
Data research scientists focus on creating reliable evaluation frameworks and high-quality training data, one of the most important elements of scientific AI development.
This role includes designing advanced scientific evaluations, acquiring external datasets, integrating experimental data into AI training systems, and developing reinforcement learning environments.
The successful applicant will work closely with computational scientists, experimental researchers, and machine learning experts to transform complex scientific processes into measurable AI benchmarking and training environments.
Key responsibilities include:
- Develop an assessment strategy across your AI training system.
- Helps identify capability gaps and define future research priorities.
- Transform scientific workflows into advanced benchmarking and reinforcement learning environments.
- Build datasets that improve AI model performance and scientific reasoning capabilities.
- Create a feedback system that connects scientific applications, model evaluation, and training improvements.
main responsibilities
Selected Research Scientist Data will be responsible for managing data and evaluation efforts across Periodic Labs’ artificial intelligence research pipeline.
The main duties are:
- Collaborate with scientific experts to design rigorous evaluations of your AI models.
- Obtain and evaluate datasets covering chemistry, physics, materials science, mathematics, simulation, and laboratory technology.
- Develop scalable data pipelines to process large and complex datasets.
- Clean, transform, and prepare data for training large-scale AI models.
- Create tools that allow researchers to analyze datasets and identify weaknesses in models.
- Support research teams in determining future evaluation requirements and data development priorities.
Required qualifications and skills
Periodic Labs seeks candidates with a strong background in artificial intelligence research, data engineering, or scientific computing.
The ideal applicant should demonstrate the following experience:
- Evaluate, benchmark, or design reinforcement learning environments for AI systems.
- Developing large-scale language models, AI agents, and datasets for scientific applications.
- Building large-scale data processing pipelines.
- Manage dataset quality, provenance, licensing considerations, and contamination risks.
- Implement data versioning and lineage tracking systems.
- We conduct research through repeated experimentation, analysis, and improvement.
Candidates with research experience in scientific fields such as materials science, solid state chemistry, chemistry, computational physics, and semiconductor research are encouraged to apply.
IDEAL CANDIDATE PROFILE
The successful candidate will have a research-driven approach and the ability to investigate complex problems through experimentation and data analysis.
You should be comfortable collaborating across disciplines and working with experts in both artificial intelligence and the physical sciences.
This role requires someone who can combine technical expertise and scientific curiosity to create datasets and evaluation systems that improve the ability of AI models to support scientific discovery.
Position details and compensation
The Data Researcher position offers competitive compensation and benefits, including:
- Employment type: Full-time employee
- Location: Menlo Park, California, USA or Montreal, Canada
- Future location options: San Francisco
- Minimum education: Bachelor’s degree or equivalent experience
- Salary range: $250,000 – $350,000 plus capital
- Visa Sponsorship: Available to qualified candidates
Career opportunities in scientific AI
Periodic Labs’ Research Scientist, Data role provides an opportunity for researchers and engineers to contribute to the future of artificial intelligence-driven scientific discovery.
By developing advanced datasets, evaluation systems, and AI research infrastructure, the successful candidate will play a key role in improving machine learning models designed to address some of the world’s most complex scientific challenges.
Periodic Labs welcomes applications from individuals passionate about AI research, scientific innovation, and building technology that expands the boundaries of human knowledge.
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Disclaimer: Global South Opportunities (GSO) is not the organization providing this opportunity. For inquiries, please contact the official organization directly. Please do not submit your application or resume to GSO as it cannot be processed by GSO. Due to the large volume of emails we receive every day, we may not be able to respond to all inquiries. Thank you for your understanding.
