Tinder connects people. With tens of millions of members and a presence in every country on earth, our reach is expansive — and rapidly growing. Your daily work here will cause millions of people to spark new and meaningful connections. Our small, dedicated engineering team has one of the highest ratios of members to engineers in the industry, which makes every member of the team critical to our success. You'll have a unique opportunity to join a company with a global footprint while working on a team that's small enough for you to feel the impact each day.

Trust & Safety Team

Tinder T&S team is responsible for systems, algorithms, and policies that keep Tinder and its members safe. It is a multi-disciplinary team of Engineers, ML Practitioners, and Policy Experts.

In this role you will:

• Build and maintain systems that enable the T&S team to detect and remediate bad actors on the platform. You will use techniques that spread across many ML fields: Deep Learning, Anomaly Detection, Qualitative/Quantitative Analytics, etc and will leverage large amounts of text, photos, and app behavior data. 
• You will integrate closely with T&S policy, product, and engineering teams. You will be responsible for proposing new algorithms and get them integrated into the T&S ecosystem.
• Collaborate with policy, and the product team to establish how an algorithm will be used.
• Collaborate with engineering teams to put the algorithm in production.

Who you are:

• Successful candidates will have a learning mindset, care about problems more than the technique, are self-motivated, and collaborative.
• Experience with at least one popular machine learning framework. Preferably Tensorflow.
• Experience with modern machine learning techniques. Preferably Supervised Learning using Deep Learning, and Anomaly Detection.
• Experience with modeling text, and photos data.
• Experience with Qualitative/Quantitative analytics at scale. Preferably using modern tools like Spark.

Qualifications:

• BS/MS/Ph.D in Computer Science or related field.
• Proficiency in at least one popular programming language. Preferably Python.
• Working knowledge of AWS, Kubernetes, and Docker.
• 1+ years of professional experience developing, and deploying ML models.