Campus AI Research Engineer (Full-Time)

02 · Requirements and your timeline

Check the actual eligibility requirements.

Work arrangement

Not stated — confirm with the employer

Degree levels recorded

unknown

Graduation window

No graduation window stated

Start date

Not confirmed

Deadline

No fixed deadline confirmed

Minimum years of experience

No numeric minimum confirmed

No fixed closing date stated in the reviewed official job description. Apply while listed and confirm availability before preparing an application.

Deep expertise in Python/C++, PyTorch/JAX/TensorFlow, accelerator/GPU programming and large-scale AI/ML systems. Degree level and graduation window are not stated.

• The posting states full-time work-visa sponsorship; confirm visa category and individual eligibility with recruiting.
• The campus posting does not state an explicit degree level or graduation window; confirm undergraduate/master's eligibility with recruiting.
• Research-intensive specialist role; prioritize only with strong AI/ML systems or publication evidence, not as a general business-analytics option.

Questions still to resolve

• E-Verify enrollment has not been matched to the hiring legal entity. Verify separately if relevant to STEM OPT.
• Compare your degree and enrollment status with the official requirements.
• No fixed deadline was confirmed; check the official posting before applying.

Graduation dates are compared only where exact bounds are stated. We do not infer CPT/OPT eligibility, work permission or H-1B timing from your academic year.

03 · Separate employer check

E-Verify

Hiring legal entity not independently confirmed

The hiring legal entity has not been matched to official E-Verify records; absence of a match is not evidence of non-enrollment.

E-Verify participation is separate from H-1B sponsorship and the other STEM OPT requirements. An employer statement or an unmatched name is not a completed legal-employer verification.

04 · Prepare with a purpose

What to prepare and why

Present an AI/ML project with reproducible experiments, baseline comparisons, scale/performance evidence and implementation trade-offs; prepare to discuss papers or open-source contributions where required.