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Staff Machine Learning Engineer

Wonder
New York City, USA
Full-time
Senior
Onsite
Discovered 5 days ago

Full Job Posting

Benefits

  • The benefits applicable to this role include a competitive compensation package with equity and a 401(k). We also offer a choice of medical, dental, and vision plans, company paid short and long term disability coverage, paid time off including flexible time off for exempt employees, paid vacation for non-exempt employees, and paid sick leave in compliance with applicable law in addition to paid parental leave, discounted meals and exclusive perks across the Wonder family of brands.
  • Eligibility, effective dates, and available plan options vary by employment classification and location. To learn more about benefits for this role, visit our Careers page here.
  • A Final Note
  • At Wonder, we build the best teams by hiring with an objective lens — evaluating people for their potential while championing diversity, equity, and inclusion. We do not discriminate based on race, color, religion, gender identity or expression, sexual orientation, national origin, age, military service eligibility, veteran status, marital status, disability, or any other protected class. As part of our commitment to fair and compliant hiring practices, Wonder participates in the federal government's E-Verify program to confirm employment eligibility. If you need an accommodation during the interview process, please let your recruiter know.
  • We look forward to hearing from you! We'll contact you via email or text to schedule interviews and share information about your candidacy.

Qualifications

  • Experience with Large Language Models and transformer-based architectures, including fine-tuning, embedding generation, and deploying them in latency-sensitive applications. Experience with language understanding over imperfect grammar (real-world search queries, menu and catalog text) is a strong plus
  • Experience with cloud ML infrastructure (AWS/SageMaker or equivalent), model deployment, and production monitoring and observability

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