Deep Learning Engineer, Recommender System at

About helps fashion e-commerce uncover consumer’s shopping preferences and provide personalized shopping experiences to consumers. We’re making e-commerce merchants have enterprise-level AI supporting on day 1. We’re making merchant’s work better and we need people like you to help us.

If you’re interested in this position, please email your cv to or

Deep Learning Engineer, Recommender System at

We are seeking Deep Learning Engineers, dedicated to Recommender System, to join our data science team. The ideal person will have industry experience working on a range of classification and optimization problems, rate prediction, recommendation systems, or search ranking. The position will involve taking these skills and applying them to some of the most exciting and massive shopping data and prediction problems that exist on the web. You will bring the ability to own the whole ML life cycle, define projects, and drive excellence across teams.


  • Develop and maintain the core recommender system and analytical reports.
  • Lead deep learning model deployment and production execution.
  • Collaborate with product owners and business development teams to ensure the quality and stability of data-driven products.
  • Collaborate with backend team, data engineers, and data annotators.
  • Conduct research in the domain of recommender systems.
  • Implement new recommender system models.
  • Maintain all workflow documents of data-driven products up to date.


  • Healthcare (Medical, Dental, Vision)
  • Retirement savings or 401(K)
  • Paid time off (Annual; 10 days)
  • Maternity/Paternity leave
  • Life insurance


  • Tuition reimbursement and training
  • Personal facilities (Laptop, Screen)
  • Free health screening
  • Free snacks and drinks
  • Open and creative environment
  • Irregular dinner/outing, happy hours
  • Extended annual time off
  • Paid time off to volunteer (Birthday, Menstrual, Funeral)
  • Flexible schedules and working time
  • Remote working optionally
  • Employee stock ownership plan (ESOP)

Culture and 6 Core Values at

Grit – We thrive outside of our comfort zone, pushing ourselves to go even further. We think long-term and constantly strive to be better, even if things don’t always go as expected.

Trust – We earn that trust by listening to each other, following through with our commitments, and keeping our words. We exercise transparency within the company, our customers, and our community.

Humility – We learn from everyone and everywhere, and we approach each new challenge knowing that we may not have all the answers.

Empathy – We craft our intention to keep curious about the industry, business, and practical scenarios that we purify the insights and forge the approaches.

Candor – We are open and honest. We give each other praise and criticism because we believe each team member is as important as the other.

Craftsmanship – We simplify, innovate, perfect, and start over until everything we touch enhances each life it touches.


Taipei City
(Near Nanjing Sanmin MRT Station)



[Minimum Qualifications]

Background & Skills:
  • Minimum 2 years experience in deep learning and recommendation systems.
  • Minimum 2 years of relevant and hands-on experience in:
    • Basics: Linux, Python, Git
    • Data Science: PyTorch, Numpy, Pandas, Scikit-Learn, SQL
  • Expert knowledge developing production-level ML products.
  • Experience in demonstrating technical leadership working with teams, owning projects, defining and setting technical direction for projects.
Working Style:
  • Excellent communication and collaborative skills.
  • Strong problem-solving and conceptual thinking skills.
  • Flexibility and comfort working in a dynamic, team environment with a possible remote organization with minimal documentation and process.
[Preferred Qualifications] (Optionally; the more, the better)

Background & Skills:
  • MS degree in Computer Science or related quantitative field with 2-3+ years of deep learning-related work or research.
  • Knowledge in natural language processing or computer vision.
  • Experience with cloud services, such as AWS, S3, EC2, Beanstalk, Redis.
  • Experience with filesystems, server architectures, and distributed systems.
  • Experience with Elasticsearch.
  • Publications in top conferences, including but not limited to: ACL, EMNLP, CVPR, ECCV, RecSys, WWW, ICML, IJCAI, and AAAI.