Machine Learning Implementation Engineer at OfferFit
Remote, Oregon, USA -
Full Time


Start Date

Immediate

Expiry Date

19 Jul, 25

Salary

129000.0

Posted On

19 Mar, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Addition, English, Base Pay, Eligibility

Industry

Information Technology/IT

Description

OfferFit was founded by ex-McKinsey and BCG math PhDs, and we’re funded by leading Silicon Valley VCs. OfferFit’s AI decisioning engine supports 1:1 personalization for lifecycle marketing campaigns, powered by reinforcement learning AI. This allows marketers to test & improve the performance of their campaigns much faster than before. Customers include leading brands like Brinks Home, Yelp, Chime, Engie, and MetLife, among many others.

POSITION OVERVIEW:

As our customer base continues to grow with the excitement around our product, we’re looking to expand! Come join our Machine Learning Implementation Engineering team of creative technical experts who collaborate with customers to ensure their success with OfferFit!

ADDITIONAL REQUIREMENTS:

  • Candidates must be able to fully overlap and support North America time zones
  • Must be fluent in English, both written and verbal
  • Up to 10-15% travel for company-wide quarterly gatherings, team offsite workshops, customer meetings, and industry-related events
    The base salary range for this position in the United States is $112,000 - $129,000 per year plus eligibility for additional bonus ranging $13,000-$15,000; Eligibility for an end of year performance bonus, commissions (when applicable) and/or equity options may be provided as part of the compensation package, in addition to a full range of medical, financial, and/or other benefits, depending on the position offered. For non-US based candidates, base pay and overall compensation packages will be adjusted based on location. Applicants should apply via OfferFit’s internal or external careers site.
Responsibilities

Please refer the Job description for details

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