Sonatus
Staff System Test Automation Engineer, AI
Sunnyvale, CA · Staff+
Stay score
odds of building a lasting career here
Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
Personalize to your clock →H-1B wage level
the lottery is wage-weighted — each level is one more entry
$11,244 more — $159,744 — moves this role to Level III and 3 lottery entries. That figure is inside the range the employer already advertised.
Based on the DOL prevailing wage for this occupation and worksite, a base salary of $159,744 would place this position at wage Level III. That figure is within the posted range, and I'd like to target it. This role classifies under "Software Quality Assurance Analysts and Testers" for prevailing-wage purposes.
DOL prevailing wage, 2026-27 wage year · Software Quality Assurance Analysts and Testers (15-1253) · San Jose-Sunnyvale-Santa Clara, CA. Wage level is derived by USCIS from the offered wage, occupation and worksite; the occupation shown is inferred from the job title.
Employer immigration record
from this employer's Department of Labor filings
Files H-1B transfers
Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.
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About the role
- This role centers on validating AI model testing, deployment, monitoring, and test automation within innovative automotive and cloud environments.
- This is a hybrid role where you must come into our Sunnyvale HQ office 3 days a week.
- You will be reporting to our Senior Manager, Engineering (QA Automation - Vehicle).
Responsibilities
- Collaborate with diverse teams to define testing strategies for challenging and innovative projects.
- Develop and execute detailed test plans using both automated and manual testing.
Requirements
- Minimum 5 years of experience in embedded software in data centers, networking, security, cloud services, or the automotive industry, and minimum 1+ year of experience with AI model development and testing.
- Proficient in test methodologies with experience in system-level testing of physical devices.
- Familiarity with the Linux command line and proficiency in shell scripting.
- Familiarity with Quality Assurance for the Cloud Platform developed on private and public Cloud Infrastructure.
- Strong expertise in AI fundamentals, neural networks, and model deployment and ML flow.
- Strong experience in E2E testing of cloud-controlled / monitored embedded products.
- Experience with designing and implementing test tools for AI/ML automation and deployment
- Familiarity with common test frameworks such as PyTest, Playwright, Behave, or similar.
- Proficiency in JIRA, Jenkins, or similar tools.
- Review and challenge product requirements, design, and functional specifications.
- Proficient in developing, reviewing, and modifying Python scripts to enhance test coverage.
- Sunnyvale HQ Benefits & Perks Offered:
Nice to have
- A bachelor's degree (MS preferred) in computer science, computer engineering, electrical engineering, or other related fields is required.
- Familiarity with test tools in the data analytics space is a plus.
Compensation
- $148,500 - $204,000 USD
Benefits
- Health care plan (Medical, Dental & Vision)
- Flexible and Dependent Care Expense program
- Retirement plan (401k)
- Life Insurance (Basic, Voluntary & AD&D)
- Unlimited paid time off per year, 14+ paid holidays
- Wellness benefit allowance
- Phone & Internet reimbursement
- Identify areas for improvement in test coverage and methodology, providing valuable suggestions.
Company info
- At Sonatus, we're driving the transformation to AI-enabled software-defined vehicles.
- Act as the gatekeeper for each release, representing the voice of customers and providing constructive feedback.
This listing is sourced directly from Sonatus's careers page and normalized into a canonical job model.