Distributed Systems Engineer

Distributed Systems Engineer (Horovod & Ray) - ML Optimization Engine for the Future!This Jobot Job is hosted by: Eric EmenhiserAre you a fit? Easy Apply now by clicking the "Apply Now" button and sending us your resume.Salary: $140,000 - $180,000 per year

A bit about us:We spun out of widely acclaimed open source ML projects innovated by Top Engineers from the likes of AWS, Uber, VMWare, Cloudflare, Google, etc. Now that we are out of Stealth Mode, we are taking our ML optimization engine to market! Most have been finding us by the way of getting involved in our open source contributions, but here is a chance to find out what we are really building for a new generation & stack of Machine Learning products after launch!Are you a Distributed Systems specialist, who has significant experience experimenting in open sourced ML Distributed Training Systems such as Horovod or Ray? If so, please keep reading

Why join us?Our work is "mysterious and important" (an attempt at a joke for those who have watched the show 'Severance' on Apple+)!

• 100% remote org, with folks collaborating in different time zones, most residing on Pacific Time Zone.
• There are no shortages of hard problems to solve collaborating with a team who is here to help!
• Mission driven org that is not a fan of burning people out or forcing tight deadlines. Flexible time off!

Job DetailsIs your background a fit? Apply if you meet this criteria:Bachelor's in Computer Science and the following:

• 3 - 7 years of experience solving challenging Distributed Systems problems!
• Open source contributions or production setting experience with Horovod.ai and/or Ray.io
• You have strong design and coding skills in one or more of the following: Python, Ruby, Spark, Go, Typescript, Java
• Strong interest in or past project experience in MLOps

Bonus skills (desired but not required):

• Previous completed MLOps/ML Infrastructure projects, partnering with ML/AI teams
• ML: Supervised/Unsupervised, Pytorch, Tensorflow, Keras
• Experience in containerization technologies: Docker & Kubernetes
• Kafka, Apache Spark, SparkML, etc.

Interested in hearing more? Easy Apply now by clicking the "Apply Now" button.