About Slickdeals:
On a mission to bring the joy of discovering a great deal to shoppers, Slickdeals thrives on the active participation of its 12-million-strong community. Users share, upvote, and uncover the best prices on popular products from trustworthy brands.
With a robust track record of 24 years in business, marked by profitability and a thriving community that has saved more than $10 billion, Slickdeals is currently undergoing a transformation. As we evolve into a daily shopping destination for millions more, joining Slickdeals presents an exciting opportunity for entrepreneurially-minded builders to create an innovative deal discovery platform.
The Purpose:
We are building a new search, discovery, and shopping graph platform that powers the suite of Slickdeals product lines. We're looking for top-notch search engineering talents in the area of information retrieval, search indexing, Elasticsearch, Lucene, algorithms, relevance & ranking, data mining, machine learning, data analysis & metrics, query processing, multi-lingual search and multi-modal search.
In this role, you will be part of our unique Search and Discovery team. We are looking for someone with expertise in information retrieval and a passion for creating exceptional search experiences. The ideal candidate will have a strong technical background and a deep understanding of search algorithms and systems. As an individual contributor to the Search and Discovery Team, you will play a crucial role in enhancing our search capabilities and driving the discovery of deals and content for our users. You will be building products using technologies such as AWS SageMaker, Tensorflow, Pytorch, LLM, Elastic Search, REST web services, SQS/Kafka, Vector Database, HBase, Machine Learning, and more.
What We're Looking For:
- 5+ years of building, scaling and maintaining software systems in production environments
- Solid fundamentals in algorithms, data structures, system design
- Experience with machine learning frameworks and libraries (PyTorch, Tensorflow)
- Experience designing fault-tolerant distributed systems
- Strong architectural skills
Preferred Experience:
- Experience working with a cloud technology stack (AWS, GCP, Kubernetes)
- Experience building machine learning training pipelines or inference services in a production setting
- Experience with managing and maintaining open source machine learning libraries
- Experience managing small teams in pursuit of an ambitious technical goal
- Hands-on experience in building machine learning-based search and recommendation systems
- Experience with implementing ML-based search systems such as query classification, learning to rank or machine-learned ranking (MLR) search is a plus
LOCATION: Las Vegas, Los Angeles, or the Bay Area (San Mateo)
Flexible hybrid schedule visiting our Las Vegas, Los Angeles or San Mateo office.
Slickdeals Compensation, Benefits, Perks:
- Competitive salary and equity
- Competitive paid time off in addition to holiday time off
- A variety of healthcare insurance plans to give you the best care for your needs
- 401K matching above the industry standard
- Professional Development Reimbursement Program and LinkedIn Learning Membership
Work Authorization:
Candidates must be eligible to work in the United States.
Slickdeals is an Equal Opportunity Employer; employment is governed on the basis of merit, competence and qualifications and will not be influenced in any manner by race, color, religion, gender (including pregnancy, childbirth, or related medical conditions), national origin/ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability or any other protected status. Slickdeals will consider qualified applicants with criminal histories consistent with the "Ban the Box" legislation. We may access publicly available information as part of your application.
Slickdeals participates in E-Verify. For more information, please refer to E-Verify Participation and Right to Work.
Slickdeals does not accept unsolicited resumes from agencies and is not responsible for related fees.
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