D. level Machine Learning User Research Scientist to contribute to global data collection efforts and optimize programs for the consumer electronics industry. Responsibilities : • Supporting a ...
D. level Machine Learning User Research Scientist to contribute to global data collection efforts and optimize programs for the consumer electronics industry. Responsibilities : • Supporting a ...
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D. level Machine Learning User Research Scientist for our Data Sciences Practice in Los Angeles, CA . In this role, you will work as part of a team to plan and execute global data collection efforts ...
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Entry Level Machine Learning Data Annotation information
See Los Angeles, CA salary details
$12.43 - $14.39
2% of jobs
$14.39 - $16.34
10% of jobs
$17.72 is the 25th percentile. Wages below this are outliers.
$16.34 - $18.30
18% of jobs
The median wage is $19.84 / hr.
$18.30 - $20.25
25% of jobs
$20.25 - $22.20
16% of jobs
$22.92 is the 75th percentile. Wages above this are outliers.
$22.20 - $24.16
11% of jobs
$24.16 - $26.11
8% of jobs
$26.11 - $28.07
4% of jobs
$28.07 - $30.02
2% of jobs
$30.02 - $31.98
2% of jobs
$31.98 - $33.93
1% of jobs
$12
$21
$33
How much do entry level machine learning data annotation jobs pay per hour?
What is the difference between Entry Level Machine Learning Data Annotation vs Entry Level Data Labeling Specialist?
| Aspect | Entry Level Machine Learning Data Annotation | Entry Level Data Labeling Specialist |
|---|---|---|
| Credentials | Basic understanding of data annotation tools, no formal certification required | Similar; often no formal certification needed |
| Work Environment | Remote or on-site, working with AI teams and datasets | Remote or on-site, focusing on labeling data for AI/ML projects |
| Industry Usage | Primarily in AI, machine learning, and data science companies | Used across tech, automotive, healthcare, and other industries |
| Search & Comparison Intent | Commonly compared for entry-level roles in AI data prep | Often compared as a similar entry-level data labeling role |
Both roles involve preparing data for machine learning models, with similar entry-level requirements. The main difference lies in terminology and specific job focus, but they often overlap in skills and work environment.
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- Entry Level Technical

Full-time
Re-posted 25 days ago
Job description
Exponent is a premium engineering and scientific consulting firm dedicated to solving unique challenges for clients. They are seeking a Ph.D. level Machine Learning User Research Scientist to contribute to global data collection efforts and optimize programs for the consumer electronics industry.
Responsibilities:
• Supporting a range of consulting activities related to large-scale local and global programs to build custom datasets for machine learning algorithms including protocol development, data collection, data management, and analysis
• Providing operational support for prototype hardware and software systems including system validation and troubleshooting
• Actively solving technical and logistical problems in a fast-paced environment
• Creating and leading ad hoc interdisciplinary teams comprised of consultants from Data Sciences, Human Factors, Health Sciences, and Engineering Sciences
• Developing data analysis and visualization tools related to project management, demographics, and human-centered data
• Developing and maintaining client relationships
Qualifications:
Required:
• Ph.D. in Electrical Engineering, Computer Engineering, Physiology, Human Factors, or a related engineering/scientific field (such as Applied Mathematics, Computer Science, Cognitive Science, Applied Physics, Industrial Engineering, Mechanical Engineering, Civil Engineering, or Robotics)
• Ability to take an ambiguous question, use data to draw insights, and convey the results to a wide range of audiences
• Demonstrated experience and expertise in one or more of the following areas: Advanced sensing technology, Networking data analysis and visualization, Designing and executing user research studies with human subjects, using appropriate quantitative and qualitative methods to produce strategic and actionable insights that inform design and development, Experience in programming or scripting languages like Python, Shell, Java, Perl, MATLAB, Experience in instrumentation, data acquisition, and /or data processing, Operations optimization, Machine learning data set design or optimization, Dynamic system modeling and control
• Experience with human subjects research or direct collection of large experimental data sets to support the development of machine learning or artificial intelligence algorithms
• The desire to work with a diverse set of clients and engage in work outside of the traditional data science field
• Strong practical engineering ability combined with leadership and project management skills
• Excellent verbal and written communication skills
• Ability to work independently and in multidisciplinary teams
• Ability to travel to a variety of global locations to support project work (up to 40% travel)
• Presently legally authorized to work in the United States; no immigration sponsorship or processing required
Company:
With over 90 scientific and engineering disciplines, Exponent’s staff of approximately 900, located in 20 offices throughout the USA Founded in 1967, the company is headquartered in Menlo Park, USA, with a team of 1001-5000 employees. The company is currently Late Stage.
About Exponent Partners
Sourced by ZipRecruiter
Industry
It services
Company size
11 - 50 Employees
Headquarters location
San Francisco, CA, US
Year founded
2005