This content was reproduced from the employer’s website on November 14, 2022. Please visit their website below for the most up-to-date information about this position.
About the Role
Are you passionate about making Twitch safer, more inclusive, and a nicer place to enjoy? You will do exactly that in our team! You will be part of a growing Machine Learning team which develops and deploy algorithms that are the first line of defense of users’ safety at Twitch. You will work with passionate co-workers who live Twitch’s mission and put their hearts into their work. If this sounds like an environment where you will succeed then come to our team!
You can work in San Francisco, Irvine, CA; Seattle, WA; New York, NY; and Salt Lake City, UT.
You Will:
- Build machine learning (ML) products to protect Twitch users from bad behavior such as spam, phishing, and violent or illegal content
- Develop scalable machine learning infrastructure for deploying ML models on petabytes of data.
- Develop data pipelines at scale.
- Develop tools for robustly training, integrating, and monitoring ML models and services in the wild
- Help determine best practices for the usage of machine learning models in production to serve our community
- Build tools and infrastructure that empower applied scientists to prototype and deploy machine learning models in response to trust and safety concerns
- Partner with fellow engineering and science teams to accomplish complex projects together
You Have:
- Have 3+ years industry software engineering experience
- Have 2+ years of work experience building large-scale production Machine Learning systems or distributed systems
- Have experience building distributed services or ML applications and scaling computation to thousands of machines in Cloud technologies (e.g AWS, GCP) with containerization software (e.g. Docker, Kubernetes, Mesos)
Bonus Points
- Experience working with large scale data processing and orchestration tools such as Airflow and Kubeflow
- Experience with streaming data and event-driven systems, and tools like Kinesis, Kafka, Flink, and Spark
- Experience with ML frameworks such as Keras, Tensorflow, and AWS Sagemaker
