MaxLinear
AI Intern
Carlsbad, CA, US · Intern · Internship
Sponsorship not specified$25k-$40kDetected 744 days ago
PythonLLMsJiraConfluence5G/LTEPremiere ProLeadershipCommunication
About the role
- This internship will be based in office and not remote.
- Company Overview MaxLinear is a global, NASDAQ-traded company (MXL) where the entrepreneurial spirit is alive and well.
Responsibilities
- MaxLinear began by developing the world's first high-performance TV tuner chip using standard CMOS process technology.
- Others said we couldn't achieve the extremely high-performance requirements using CMOS, but we proved them wrong and achieved enduring global market leadership with our designs.
- Since then, we've developed a full line of products that drive 4G and 5G infrastructure; enable data center, metro and long-haul optical interconnects; bring 10Gbit to the home; power the IoT revolution; and enable robust and reliable communication in harsh industrial environments.
- Over the years, we've expanded through organic growth and through several acquisitions that have perfectly complemented our existing portfolio and enabled us to deliver complete end-to-end solutions in our target markets.
- We also have major design centers in Irvine and San Jose, California; Valencia, Spain; Bangalore, India; Munich, Germany; Israel; and Singapore.
- Together, we form a high-energy business team that is focused on building the best and most innovative products on the market.
Nice to have
- MaxLinear is seeking an AI Intern to work with our Software team.
- In this internship, you will focus on the following:
- Masters student preferred
Compensation
- $25.00 to $40.00 per Hour The actual starting pay will be determined by the match to certain role-related criteria such as educational degree(s) or equivalent, relevant work experience, skillset needed for the role, and geographic location; this is not an all-inclusive list as some roles may require unique skills or experience.
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This listing is sourced directly from MaxLinear's careers page and normalized into a canonical job model.