Job Description
SLAC Job Postings
Position Overview
SLAC National Accelerator Laboratory seeks an AI software engineer with experience in large language model (LLM) deployment. SLAC is one of the world¿s premier research laboratories, with internationally leading capabilities in photon science, accelerator physics, high energy physics (HEP), and energy sciences.
Machine learning is expected to play an important role in nearly every major project at SLAC, and the ML department supports discovery across SLAC¿s science mission. We are looking for a full-stack software engineer to support data management, deployment, and life-cycle management of LLM assistants for SLAC¿s scientific facilities. The role is interdisciplinary and collaborative, and the candidate will work jointly with scientists and engineers at SLAC, academics at Stanford, industrial partners in Silicon Valley, and the product will support facility users from around the world.
Given the nature of this position, on-site and hybrid work options are preferred.
Your Specific Responsibilities Include
Work with multiple domain science teams to prepare multimodal datasets from logbooks, wikis, scientific manuals, papers, etc.
Implement LLM pipelines based on open-source models. Should have knowledge of RAGs, knowledge graphs, fine-tuning, etc.
Oversee deployment of LLM applications for SLAC¿s scientific facilities
Engage with researchers at SLAC and Stanford who are developing novel LLM methodology and incorporate into active deployments
Applicants should include a cover letter, a curriculum vitae, and names of three references for future letters of recommendation with the application.
Note: This is a 36-month fixed term position with the possibility of extension or conversion to regular continuing position contingent on project fundings and needs.
We are looking for candidates, with the following criteria in mind:
Bachelor¿s degree in computer science, electrical engineering, applied mathematics, or other fields of science, and at least two years of experience in the followings:
Strong background in large language model deployment and machine learning.
Excellent verbal and written communication skills and the ability to convey complex technical concepts.
Ability to work and communicate effectively with a diverse population.
Ability to collaborate across organizations and manage/lead cross-functional efforts.
In Addition, Preferred Requirements Include
Demonstrated experience in LLM deployment
Masters¿ degree in computer science, electrical engineering, or related field.
2+ years data analysis experience with scientific data.
1+ year experience working at a DOE laboratory.
SLAC Employee Competencies
Effective Decisions : Uses job knowledge and solid judgment to make quality decisions in a timely manner.
Self-Development : Pursues a variety of venues and opportunities to continue learning and developing.
Dependability : Can be counted on to deliver results with a sense of personal responsibility for expected outcomes.
Initiative : Pursues work and interactions proactively with optimism, positive energy, and motivation to move things forward.
Adaptability : Flexes as needed when change occurs, maintains an open outlook while adjusting and accommodating changes.
Communication : Ensures effective information flow to various audiences and creates and delivers clear, appropriate written, spoken, presented messages.
Relationships : Builds relationships to foster trust, collaboration, and a positive climate to achieve common goals.
Physical Requirements And Working Conditions
Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of the job.
Work Standards
Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations.
Promote Culture of Safety: Demonstrates commitment to personal responsibility and value for environment, safety and security; communicates related concerns; uses and promotes safe behaviors based on training and lessons learned. Meets the applicable roles and responsibilities as described in the ESH Manual, Chapter 1¿General Policy and Responsibilities:
Subject to and expected to comply with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in the University's Administrative Guide, http://adminguide.stanford.edu
Classification Title: Staff Engineer 2
Job code: 0132
Supervisory Level: 20
Employment Duration/status: Fixed term 36 months
The expected pay range for this position is $116,000 to $170,000 per annum. SLAC National Accelerator Laboratory/Stanford University provides pay ranges representing its good faith estimate of what the university reasonably expects to pay for a position. The pay offered to a selected candidate will be determined
Position Overview
SLAC National Accelerator Laboratory seeks an AI software engineer with experience in large language model (LLM) deployment. SLAC is one of the world¿s premier research laboratories, with internationally leading capabilities in photon science, accelerator physics, high energy physics (HEP), and energy sciences.
Machine learning is expected to play an important role in nearly every major project at SLAC, and the ML department supports discovery across SLAC¿s science mission. We are looking for a full-stack software engineer to support data management, deployment, and life-cycle management of LLM assistants for SLAC¿s scientific facilities. The role is interdisciplinary and collaborative, and the candidate will work jointly with scientists and engineers at SLAC, academics at Stanford, industrial partners in Silicon Valley, and the product will support facility users from around the world.
Given the nature of this position, on-site and hybrid work options are preferred.
Your Specific Responsibilities Include
Work with multiple domain science teams to prepare multimodal datasets from logbooks, wikis, scientific manuals, papers, etc.
Implement LLM pipelines based on open-source models. Should have knowledge of RAGs, knowledge graphs, fine-tuning, etc.
Oversee deployment of LLM applications for SLAC¿s scientific facilities
Engage with researchers at SLAC and Stanford who are developing novel LLM methodology and incorporate into active deployments
Applicants should include a cover letter, a curriculum vitae, and names of three references for future letters of recommendation with the application.
Note: This is a 36-month fixed term position with the possibility of extension or conversion to regular continuing position contingent on project fundings and needs.
We are looking for candidates, with the following criteria in mind:
Bachelor¿s degree in computer science, electrical engineering, applied mathematics, or other fields of science, and at least two years of experience in the followings:
Strong background in large language model deployment and machine learning.
Excellent verbal and written communication skills and the ability to convey complex technical concepts.
Ability to work and communicate effectively with a diverse population.
Ability to collaborate across organizations and manage/lead cross-functional efforts.
In Addition, Preferred Requirements Include
Demonstrated experience in LLM deployment
Masters¿ degree in computer science, electrical engineering, or related field.
2+ years data analysis experience with scientific data.
1+ year experience working at a DOE laboratory.
SLAC Employee Competencies
Effective Decisions : Uses job knowledge and solid judgment to make quality decisions in a timely manner.
Self-Development : Pursues a variety of venues and opportunities to continue learning and developing.
Dependability : Can be counted on to deliver results with a sense of personal responsibility for expected outcomes.
Initiative : Pursues work and interactions proactively with optimism, positive energy, and motivation to move things forward.
Adaptability : Flexes as needed when change occurs, maintains an open outlook while adjusting and accommodating changes.
Communication : Ensures effective information flow to various audiences and creates and delivers clear, appropriate written, spoken, presented messages.
Relationships : Builds relationships to foster trust, collaboration, and a positive climate to achieve common goals.
Physical Requirements And Working Conditions
Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of the job.
Work Standards
Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations.
Promote Culture of Safety: Demonstrates commitment to personal responsibility and value for environment, safety and security; communicates related concerns; uses and promotes safe behaviors based on training and lessons learned. Meets the applicable roles and responsibilities as described in the ESH Manual, Chapter 1¿General Policy and Responsibilities:
Subject to and expected to comply with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in the University's Administrative Guide, http://adminguide.stanford.edu
Classification Title: Staff Engineer 2
Job code: 0132
Supervisory Level: 20
Employment Duration/status: Fixed term 36 months
The expected pay range for this position is $116,000 to $170,000 per annum. SLAC National Accelerator Laboratory/Stanford University provides pay ranges representing its good faith estimate of what the university reasonably expects to pay for a position. The pay offered to a selected candidate will be determined
💡 Quick Summary
Seeking a career-building opportunity? The Software Engineer - Machine Learning position is now open for candidates interested in the Software Developer Jobs sector. This role in California City offers a professional environment and growth potential.
Requirement Snapshot: Candidates should possess basic communication skills, a proactive attitude, and the ability to work in a team. Experience in Software Developer Jobs is a plus.
