Position: Senior/Lead ML Engineer (Recommender Systems)
Company: Data Science UA
Experience required: 5y
English level: upper

About us:
We are a team that has been responsible for some of the biggest cultural and tech successes of the last 10 years, spanning mobile, social, video, AR, gaming and music. Using computer vision, developments in mobile hardware and consumer technologies the PF engineering team is building a new generational phenomenon in social/media industry. We are backed by the top Silicon Valley VC’s (Index Ventures, Founders Fund and others) and some of the most influential strategic investors in media, tech, and pop culture. We just raised a Series A funding round and are ready to launch our product till the end of this year and come out of the stealth mode.

The Role - Recommendations Engineering:
We are looking for a highly motivated and creative engineer to play a core role in developing our recommendations and personalization infrastructure from scratch. You will be working with our product and analytics teams to develop a unique curation of relevant content. Whether its search, user understanding, or working on a user feed, we’re looking for someone who is passionate about building a fantastic user experience. You’ll be building pipelines, models, and then deploying those models quickly in a production setting. You will be a critical part of the team as the product moves out of internal beta testing and into broad public adoption.

We are looking for the brightest minds eager to work on problems that are both technically and socially challenging. Your work on building a world class social product through personalized recommendations and intuitive search results will drive success at all layers in the company. Past expertise with consumer products (Snapchat, Pinterest, Instagram, TikTok, etc.), or content retrieval services is a plus.

Tech Requirements:
- Information retrieval systems in a production environment
- Building recommendations services on a scale
- Video/Image Retrieval and search
- Good understanding of most common ML algorithms and libraries (from Gradient Boosting with XGBoost to Recurrent Nets with PyTorch) and understanding of what algorithm and approach is optimal for the specific problem
- Writing clean production quality code in Python, end to end. This means development, testing, documentation, and (critically) deployment

Tasks:
- Development of Piñata Recommender Engine from scratch for: Personalized Video Feed, Video Search and Retrieval, People you might know and other similar tasks
- Optimizing model serving for a large-scale
 Experimenting with different approaches to content recs, measuring and understand business metrics, do A/B testing, etc.

Big Plus:
- Experience in deployment any kind of recommender systems on a large scale and running whole system in production

Other:
- Good communication and soft skills (you’d be working a lot with the product team based in Los Angeles)