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Software Engineer - Machine Learning

SLAC Job PostingsPosition 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 deploymentsApplicants 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,-• Classification Title: Staff Engineer 2• Job code: 0132• Supervisory Level: 20• Employment Duration/status: Fixed term 36 monthsThe 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 based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location and external market pay for comparable jobs.
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What You Should Know About Software Engineer - Machine Learning, SLAC National Accelerator Laboratory

Are you a passionate Software Engineer with a knack for Machine Learning? If so, SLAC National Accelerator Laboratory in Menlo Park, CA, has an exciting opportunity for you! As a Software Engineer focusing on Machine Learning deployment, you’ll play a pivotal role in one of the world’s leading research laboratories. Here, machine learning is not just a buzzword; it’s expected to have a profound impact on all major projects. Imagine collaborating with domain science teams to prepare and manage multimodal datasets or overseeing the deployment of cutting-edge LLM applications that directly support scientific discoveries! You'll also work closely with talented scientists and engineers at SLAC, esteemed academics from Stanford, and dynamic industrial partners nestled in Silicon Valley. This software engineering position blends technical know-how with real-world application, meaning your contributions will support facility users from around the globe. If you thrive in a collaborative atmosphere and are eager to tackle challenging projects that drive innovation, SLAC could be your next great adventure. Don’t miss out on the chance to grow your skills, contribute to groundbreaking scientific initiatives, and be part of a team that values initiative and effective communication. Join us in making a significant impact in the realm of AI and Machine Learning.

Frequently Asked Questions (FAQs) for Software Engineer - Machine Learning Role at SLAC National Accelerator Laboratory
What are the main responsibilities of a Software Engineer - Machine Learning at SLAC?

As a Software Engineer - Machine Learning at SLAC, your main responsibilities include preparing multimodal datasets from various sources, implementing LLM pipelines based on open-source models, overseeing the deployment of LLM applications for scientific facilities, and engaging with researchers to integrate novel LLM methodologies into active deployments. This role is critical for supporting SLAC’s mission and collaborating with diverse teams.

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What qualifications are required for the Software Engineer - Machine Learning position at SLAC?

Candidates for the Software Engineer - Machine Learning position at SLAC are required to have a Bachelor’s degree in computer science, electrical engineering, applied mathematics, or a related field, along with at least two years of experience in machine learning and large language model deployment. Additionally, strong communication skills and the ability to work effectively in diverse teams are essential qualifications.

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Is prior experience in a DOE laboratory necessary to apply for the Software Engineer - Machine Learning role at SLAC?

While prior experience working at a DOE laboratory is preferred for the Software Engineer - Machine Learning position at SLAC, it is not strictly necessary. Candidates with a solid background in machine learning, strong communication skills, and a willingness to collaborate will still be considered. We value diverse experiences and perspectives that contribute to our mission.

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What is the work environment like for a Software Engineer - Machine Learning at SLAC?

The work environment for a Software Engineer - Machine Learning at SLAC is highly collaborative and interdisciplinary. You will work alongside scientists, engineers, and academic professionals, engaging in both remote and on-site work. The culture is supportive and encourages innovation, making it an exciting place to contribute to groundbreaking research and advancements in AI.

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What career growth opportunities are offered for the Software Engineer - Machine Learning position at SLAC?

SLAC offers significant career growth opportunities for the Software Engineer - Machine Learning position, including a fixed-term contract with potential for extension or conversion to a continuing role based on project needs. Employees are encouraged to pursue their learning and development through various projects and collaborations, with access to cutting-edge technology and an inspiring research environment.

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Common Interview Questions for Software Engineer - Machine Learning
Can you explain your experience with deploying large language models?

When answering this question, focus on specific projects where you implemented large language models. Detail the technologies and frameworks you used, the challenges you faced, and how you overcame them. Highlight any notable results that positively impacted your team or organization.

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What strategies do you use to prepare multimodal datasets?

Discuss the different strategies you implement when preparing multimodal datasets, such as data cleaning, feature extraction, and ensuring data compatibility across various formats. Mention your experience with different data sources and how you tailor your approach based on project needs.

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How do you collaborate effectively with cross-functional teams?

Provide examples of how you’ve successfully collaborated with diverse teams, focusing on communication methods, tools you used, and any frameworks that facilitated teamwork. It’s crucial to demonstrate your ability to adapt your communication style to different audiences.

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What do you consider when fine-tuning a language model?

Explain your approach to fine-tuning a language model by discussing hyperparameter tuning, training dataset selection, and evaluation metrics. Mention any tools or libraries you have employed and the impact that fine-tuning has had on model performance.

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Describe a technical challenge you've faced in a previous project and how you resolved it.

Share a specific technical challenge related to machine learning or data management. Describe the context, the actions you took to resolve it, and the results. Emphasize problem-solving skills and your ability to learn from challenges.

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How do you stay updated with the latest advancements in machine learning?

Discuss your methods for keeping up to date, such as attending conferences, participating in online courses, reading industry publications, or engaging with professional groups. Highlight how you apply new knowledge to your work when relevant.

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Explain your understanding of RAGs and knowledge graphs.

Clarify your understanding of retrieval-augmented generation (RAGs) and knowledge graphs, giving examples of how they can be utilized in machine learning projects. Highlight any experience you have with these concepts and their implementations.

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What is your approach to data quality when working with scientific data?

Mention your strategies for ensuring data quality, including validation processes, consistency checks, and methods for handling missing or incorrect data. Discuss the importance of data quality in scientific research and its implications.

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How would you handle differing opinions or conflicts within a team project?

Illustrate your conflict resolution style by describing a past experience where differing opinions arose. Focus on how you facilitated discussions, sought common ground, and managed to reach a consensus that benefited the project.

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What motivates you to work in the field of machine learning?

Share personal anecdotes that highlight your passion for machine learning, including specific projects that inspired you or areas of research that you find exciting. This question is an opportunity to showcase your enthusiasm and dedication to the field.

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DATE POSTED
December 17, 2024

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