Machine Learning Systems Engineer (1 Year Fixed Term)
STANFORD, CAPosted 21 days ago
Job summary
Job post source
This job is from a recruiting firm hiring for a separate company.
Job overview
The Machine Learning Systems Engineer at Stanford University will design and maintain compute infrastructure to support a Neuro-AI project modeling brain activity and cognition.
Responsibilities and impact
The role involves designing and developing specialized systems, solving complex engineering problems, contributing to research and publications, and providing technical guidance to staff and students.
Compensation and benefits
The position offers a salary range of $126,810 to $151,461 annually with competitive benefits including health insurance, retirement plans, and career mentoring.
Experience and skills
Candidates need 3+ years managing large-scale compute infrastructure, proficiency with Docker, Kubernetes or SLURM, scripting skills, Linux/Unix administration, and familiarity with big data tools and machine learning frameworks.
Career development
The job provides strong mentoring in career development within a collaborative, interdisciplinary academic environment.
Work environment and culture
The work culture is collaborative and multidisciplinary, involving teams from neuroscience, AI, and engineering with a vibrant research atmosphere.
Company information
Stanford University is a leading academic institution engaged in cutting-edge Neuro-AI research through multiple interdisciplinary labs and institutes.
Team overview
The candidate will join labs led by Andreas Tolias and Tirin Moore, known for expertise in perception, cognition, and computational neuroscience.
Job location and travel
The position is based at Stanford University with potential travel and exposure to lab environments with specialized equipment.
Application process
Applicants must submit an application, CV, and a one-page interest statement to the provided recruiting email address.
Unique job features
This role is unique for its involvement in an interdisciplinary project creating a digital twin of the brain using advanced electrophysiology and machine learning techniques.
Company overview
Stanford University is a prestigious private research university located in Stanford, California, known for its academic excellence and significant contributions to innovation and technology. It generates revenue through tuition fees, research grants, endowments, and partnerships with industry leaders. Founded in 1885 by Leland and Jane Stanford in memory of their son, the university has a rich history of fostering entrepreneurship and has been instrumental in the development of Silicon Valley.
How to land this job
Tailor your resume to emphasize your experience managing large-scale compute infrastructures, especially in machine learning contexts, highlighting skills with containerization (Docker), orchestration platforms (Kubernetes, SLURM), and cloud computing resources.
Highlight your proficiency in scripting languages such as Python and Bash, Linux/Unix systems administration, and familiarity with distributed big data tools like Apache Spark or Airflow, as these are crucial for supporting the Neuro-AI project's compute and data pipelines.
Apply through multiple channels including Stanford University's official careers page, the dedicated recruiting email (recruiting@enigmaproject.ai), and LinkedIn to maximize your application's visibility and reach.
Connect with current systems engineers, researchers, or faculty members involved in the Neuro-AI project or related institutes (Wu Tsai Neurosciences Institute, Stanford Bio-X) on LinkedIn; use ice breakers such as commenting on recent published research, expressing enthusiasm for interdisciplinary collaboration, or asking about the team's approach to integrating machine learning with neuroscience.
Optimize your resume for ATS by incorporating keywords directly from the job description like 'machine learning systems engineer,' 'compute infrastructure,' 'containerization,' 'Kubernetes,' 'Python scripting,' and 'GPU-based HPCs' to ensure your resume passes initial automated screenings.
Use Jennie Johnson's Power Apply feature to automate tailoring your resume, identify multiple application platforms, and find LinkedIn contacts to network with, allowing you to focus your time on preparing for interviews and deepening your understanding of the role.
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