Staff Scientist – Agents for Science Staff Scientist – Agents for Science Argonne National Labo[...]

Seeds Renewables; Lemont (IL); On-site; USD 94,000 - 147,000; Full time

Job summary

Argonne National Laboratory is seeking a Staff Scientist – Agents for Science to advance agentic AI systems that can reason, plan, and act across scientific data, simulations, instruments, and workflows. The role sits in a multidisciplinary AI group and offers contributions to DOE’s Genesis mission in computation and discovery.

The position emphasizes development of novel AI agents, integration with scientific software, and evaluation of usefulness and safety in scientific contexts.

Qualifications

• RD2: Bachelor’s degree and 5+ years of experience, or Master’s degree and 3+ years of experience, or PhD and 0+ years of experience
• PhD preferred
• Experience with reinforcement learning, planning, or sequential decision-making
• Strong programming skills in Python, C, C++
• Experience with PyTorch, JAX, or related tools
• Experience designing, implementing, and evaluating complex AI systems
• Good communication skills, both verbal and written
• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork

Responsibilities

• Designing and developing agentic AI systems for scientific discovery, including reasoning, planning, information retrieval, tool invocation, and multi-step tasks
• Developing agents that can interact with simulation workflows, data pipelines, scientific software, instruments, knowledge bases, and computational tools
• Building methods for tool use, workflow orchestration, memory, planning, adaptation, and decision-making in scientific environments
• Evaluating agent performance in terms of scientific usefulness, reliability, robustness, efficiency, and safety
• Conduct research and development aligned with Argonne’s strategic mission in computation, AI, and scientific discovery
• Contribute to a team culture valuing scientific excellence, collaboration, innovation, and inclusive growth

Skills

Python

C/C++

reinforcement learning

communication

Education

Bachelor’s degree (CS/ML/comp sci)

Master’s degree

PhD (preferred)

Tools

PyTorch

JAX

Job description

TheArgonne Leadership Computing Facility (ALCF) is seeking a Staff Scientist – Agents for Science to help pioneer AI systems that can reason, plan, act, and collaborate in support of scientific discovery. AI for science is entering a new phase. Beyond models that analyze data or make predictions, there is growing opportunity to develop agentic AI systems that can interact with scientific data, simulations, instruments, workflows, and knowledge sources in more adaptive and autonomous ways. At ALCF, we are exploring how such systems can help scientists formulate hypotheses, design experiments, navigate complex data, coordinate computational workflows, and accelerate discovery across disciplines.

This role is an opportunity to work at the frontier ofagentic AI, foundation models, scientific workflows, and high-performance computing, and contribute to the Department of Energy’s Genesis mission. We are looking for a creative and collaborative staff scientist/engineer who wants to develop novel methods and systems for AI agents in scientific environments and deploy them at the scale required by real scientific challenges. You will join the AI group—a highly collaborative, multidisciplinary environment and work alongside experts in AI, simulation, computer science, applied mathematics, and domain science.

Your work may include:

• Designing and developing agentic AI systems for scientific discovery, including systems that can reason, plan, retrieve information, invoke tools, and execute multi-step scientific tasks
• Developing agents that can interact with simulation workflows, data pipelines, scientific software, instruments, knowledge bases, and computational tools
• Building methods for tool use, workflow orchestration, memory, planning, adaptation, and decision-making in scientific environments
• Evaluating agent performance in terms of scientific usefulness, reliability, robustness, efficiency, and safety
• Conduct research and development aligned with Argonne’s strategic mission in computation, AI, and scientific discovery.
• Contribute to a team culture that values scientific excellence, collaboration, innovation, and inclusive professional growth.

This position qualifies as “Hybrid Remote Work - Mostly Onsite”: which applies to employees regularly scheduled for some onsite and some remote days, with employees typically working up to 40% of their time remotely.

Position Requirements

Required Qualifications:

• RD2: Bachelor’s degree and 5+ years of experience, or Master’s degree and 3+ years of experience, or PhD and 0+ years of experience, or equivalent
• Educational background in computer science, machine learning, artificial intelligence, computational science, applied mathematics , or a related field, PhD preferred
• Experience with reinforcement learning , planning, or sequential decision-making
• Strong programming skills in one or more languages such asPython, C, C++
• Experience with one or more AI frameworks such as PyTorch, JAX, or related tools
• Experience designing, implementing, and evaluating complex AI systems
• Good communication skills, both verbal and written.
• Ability to model Argonne’s core values ofimpact, safety, respect, integrity, and teamwork

Preferred Qualifications:

• Experience with LLM-based systems , retrieval-augmented generation, tool calling, or workflow agents
• Experience integrating AI systems with scientific software, simulations, databases, or experimental workflows
• Experience with distributed training , inference systems, or HPC environments

Job Family

Research Development (RD)

Job Profile

Computer Science 2

Worker Type

Regular

Time Type

Full time The expected hiring range for this position is $94,486.00 - $147,398.94.

Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.

Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.

All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case‑by‑case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.

Argonne National Laboratory, one of the U.S. Department of Energy's national laboratories for science and engineering research, employs 3,400 employees, including 1,400 scientists and engineers, three-quarters of whom hold doctoral degrees. Argonne's annual operating budget of around $1 billion supports upwards of 200 research projects. Since 1990, Argonne has worked ...