Deep Learning Research Intern - Summer 2027 - New York

02 · Requirements and your timeline

Check the actual eligibility requirements.

Degree levels recorded

bachelors · masters · phd

Graduation window

September 2027–July 2028

Start date

2027-06-07

Deadline

No fixed deadline confirmed

Minimum years of experience

No numeric minimum confirmed

No fixed application closing date is published in the reviewed JD or job-specific ATS application metadata; apply promptly and reconfirm before submitting.

Bachelor’s, master’s or PhD students in a quantitative/ML field graduating September 2027–July 2028. Applied deep-learning experience, Python and PyTorch/JAX/TensorFlow. Must start the ten-week internship in New York on June 7, 2027.

• Role-specific sponsorship and CPT/OPT support are not confirmed.
• This role requires prior applied deep-learning experience and is not a general analytics starting point.
• Salary figures are annualized rather than an internship total.

Questions still to resolve

• Confirm sponsorship for the specific role and your circumstances.
• Add your graduation month and year to compare the published window.
• Confirm enrollment in the degree program specified by this posting.
• 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

No official E-Verify legal-employer match was verified for this posting. A general E-Verify notice is not a legal-entity match or proof of H-1B support.

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

Why prepare: this is an advanced modeling option for students with strong ML evidence. Preparation: build reproducible deep-learning experiments with baselines, ablations and explicit data-leakage checks; communicate what the results do and do not show.