Machine Learning Systems Engineer (1 Year Fixed Term) at Greenfix Property Care

, California, United States -

Full Time

Start Date

Immediate

Expiry Date

20 Feb, 26

Salary

151461.0

Posted On

23 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Data Pipeline, Systems Engineering, Linux/Unix, Cloud Computing, GPU-based HPC, Containerization, Scripting, Project Management, Distributed Big Data, Neuroscience, Artificial Intelligence, Technical Expertise, Research Development, Collaboration, Neuro-AI

Industry

Household Services

Description

The Department of Ophthalmology in the School of Medicine at Stanford University is launching an interdisciplinary Neuro-AI project dedicated to building a foundation model of the brain. This endeavor will involve multiple labs and faculty across the Stanford campus, including the Wu Tsai Neurosciences Institute, Stanford Bio-X, and the Human-Centered Artificial Intelligence Institute. Leveraging cutting-edge advances in electrophysiology and machine learning, this project aims to create a functional "digital twin" - a model that captures both the activity dynamics of the brain at cellular resolution and the intelligent behavior it generates, including perception, motor planning, learning, reasoning, and problem-solving. This ambitious initiative promises to offer unprecedented insights into the brain's algorithms of perception and cognition while serving as a key resource for aligning artificial intelligence models with human-like neural representations. As part of this project, we are seeking talented systems engineers with extensive experience in large scale data and compute clusters. As a Systems Engineer, you will be responsible for designing, deploying, and maintaining the compute infrastructure that supports our machine learning and data pipeline operations. This position promises a vibrant and cooperative atmosphere within the laboratories of Andreas Tolias ( ), Tirin Moore ( ) and other labs at Stanford University renowned for their expertise in perception, cognition, pioneering neural recording techniques, computational neuroscience, machine learning, and Neuro-AI research. Duties include: Design and develop complex and specialized equipment, instruments, or systems; coordinate detailed phases of work related to responsibility for part of a major project or for an entire project of moderate scope. Develop technical and methodological solutions to complex engineering/scientific problems requiring independent analytical thinking and advanced knowledge. Develop creative new or improved equipment, materials, technologies, processes, methods, or software important to the advancement of the field. Contribute technical expertise, and perform basic research and development in support of programs/projects; act as advisor/consultant in area of specialty. Contribute to portions of published articles or presentations; prepare and write reports; draft and prepare scientific papers. Provide technical direction to other research staff, engineering associates, technicians, and/or students, as needed. - Other duties may also be assigned What we offer: Work on a collaborative and uniquely positioned project spanning several disciplines, from neuroscience to artificial intelligence and engineering. Work jointly with a vibrant team of researchers and scientists in a project dedicated to one mission, rooted in academia but inspired by science in industry. Competitive salary and benefits. Strong mentoring in career development. Application: In addition to completing the application, please send your CV and one page interest statement to: DESIRED QUALIFICATIONS: 3+ years of experience in designing, managing and running large-scale compute infrastructure in the context of machine learning Experience with containerization technologies like Docker and orchestration platforms like Kubernetes or SLURM Proficiency in scripting languages such as Python, Bash, or PowerShell Strong knowledge of Linux/Unix systems administration Ability to work effectively in a collaborative, multidisciplinary environment Familiarity with modern distributed big data tools and pipelines such as Apache Spark, Arrow, Airflow, Delta Lake, or similar Familiarity with machine learning frameworks like PyTorch or JAX In-depth experience with cloud computing resources Thorough knowledge of GPU-based HPCs in the context of machine learning. EDUCATION & EXPERIENCE (REQUIRED): Bachelor's degree and three years of relevant experience, or combination of education and relevant experience. KNOWLEDGE, SKILLS AND ABILITIES (REQUIRED): Thorough knowledge of the principles of engineering and related natural sciences. Demonstrated project management experience. CERTIFICATIONS & LICENSES: None PHYSICAL REQUIREMENTS : Frequently grasp lightly/fine manipulation, perform desk-based computer tasks, lift/carry/push/pull objects that weigh up to 10 pounds. Occasionally stand/walk, sit, twist/bend/stoop/squat, grasp forcefully. Rarely kneel/crawl, climb (ladders, scaffolds, or other), reach/work above shoulders, use a telephone, writing by hand, sort/file paperwork or parts, operate foot and/or hand controls, lift/carry/push/pull objects that weigh >40 pounds. - 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 his or her job. WORKING CONDITIONS: May be exposed to high voltage electricity, radiation or electromagnetic fields, lasers, noise > 80dB TWA, Allergens/Biohazards/Chemicals /Asbestos, confined spaces, working at heights ?10 feet, temperature extremes, heavy metals, unusual work hours or routine overtime and/or inclement weather. May require travel. The expected pay range for this position is $126,810 to $151,461 annually. 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. At Stanford University, base pay represents only one aspect of the comprehensive rewards package. The Cardinal at Work website ( ) provides detailed information on Stanford's extensive range of benefits and rewards offered to employees. Specifics about the rewards package for this position may be discussed during the hiring process. Consistent with its obligations under the law, the University will provide reasonable accommodations to applicants and employees with disabilities. Applicants requiring reasonable accommodation for any part of the application or hiring process should contact Stanford University Human Resources by submitting a contact form . Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.

Responsibilities

Design, deploy, and maintain compute infrastructure for machine learning and data pipeline operations. Develop technical solutions to complex engineering problems and contribute to research and development efforts.