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Assistant/Associate Specialist – Mummaneni Lab

Application Window

Open date: July 12, 2023

Most recent review date: Friday, Aug 11, 2023 at 11:59pm (Pacific Time)
Applications received after this date will be reviewed by the search committee if the position has not yet been filled.

Final date: Sunday, Jan 12, 2025 at 11:59pm (Pacific Time)
Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.

Position description

Assistant/Associate Specialist – Mummaneni Lab

The Department of Neurological Surgery - Spine is seeking for an Assistant or Associate Specialist. The area of specialization and responsibilities include machine learning, specifically in design and development of deep-learning models (encoding and decoding); data analysis, project management, maintaining specific protocols, data post-processing, data entry, compliance with the regulatory and UCSF policies, collecting data to meet the reporting requirements of industry and other granting agencies, making PowerPoints, assisting writing abstracts and manuscripts, be available to present talks at meetings, coordinate meeting travel and budgets with the PI, grant writing, and IRB submissions.

Required Qualifications:

  • Specialists appointed at the Assistant rank must possess (or in the process of obtaining) a master’s degree in data science, machine learning, artificial intelligence, or in a related field or a baccalaureate degree with 3 or more years of research experience.
  • Specialists appointed at the Associate rank must possess (or in the process of obtaining) a master’s degree (or equivalent degree) in data science, machine learning, artificial intelligence, or in a related field or five to ten years of experience in the relevant specialization.
  • 2+ years of experience in developing and deploying machine-learning models.
  • Applicants must have obtained the degree requirement for the Specialist rank by the time of hire.
  • Applicants' materials must list current and/or pending qualifications upon submission.

Preferred Qualifications:

  • PhD degree in data science, machine learning, artificial intelligence, or in a related field.
  • Post-graduate training in field.

Appointees in the Specialist series will be expected to engage in specialized research, professional activities and do not have teaching responsibilities. Specialists are expected to use their professional expertise to make scientific and scholarly contributions, and may participate in University and Public Service. Screening of applicants will begin immediately and will continue as needed throughout the recruitment period. Salary and rank will be commensurate with the applicants experience and training.

See Table 24B for the salary range for this position. A reasonable estimate for this position is $59,200 - $82,100.

Please apply online at

Application Requirements

Document requirements
  • Curriculum Vitae - CV must clearly list current and/or pending qualifications (e.g. board eligibility/certification, medical licensure, etc.).

  • Cover Letter

  • Statement of Research (Optional)

  • Statement of Teaching (Optional)

  • Statement of Contributions to Diversity - Please see the following page for more details: Contributions to Diversity Statement

  • Misc / Additional (Optional)

Reference requirements
  • 3 required (contact information only)

About UC San Francisco

As a University employee, you will be required to comply with all applicable University policies and/or collective bargaining agreements, as may be amended from time to time. Federal, state, or local government directives may impose additional requirements.

UC San Francisco seeks candidates whose experience, teaching, research, or community service has prepared them to contribute to our commitment to diversity and excellence. The University of California is an Equal Opportunity/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, age or protected veteran status.

Job location

San Francisco, CA