Software Engineer, Safety ML

Amazon•San Francisco, CA

10d•Onsite

About The Position

As a software engineer on Twitch’s Safety ML team, you will leverage machine learning to develop the systems that keep the everyday Twitch user safe from abuse such as harassment, spam, and illegal content. You will collaborate with a passionate team of scientists and engineers to build the systems necessary to train, evaluate, and productionize our various models. You will build the tools and systems necessary to integrate these models into various parts of the Twitch experience to protect our users from harm. Our team is based in San Francisco, CA.

Requirements

3+ years of non-internship professional software development experience
2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
1+ years of software development engineer or related occupational experience
1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience
1+ years of Object Oriented Design experience
Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
Experience programming with at least one software programming language

Nice To Haves

3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
Bachelor's degree in computer science or equivalent

Responsibilities

Design, build, and deploy reliable and performant services to protect Twitch and its users from abusive behavior such as harassment, spam, and violent or illegal content
Collaborate with Community Health’s science and product management team to productionize and scale models into ML-based services to be used across Twitch.
Design and build performant and reliable systems that handle multiple types of data, such as user behavior, metadata, and user generated content at scale for model development
Deliver high-quality code, tests, and documentation
Mentor other engineers on best practices within the ML space