Senior DevOps Engineer
Engineering | San Francisco, CA | Full Time
Figure Eight is the essential human-in-the-loop AI platform. With 10 years’ experience, we make the world’s most widely used software for Artificial Intelligence. We have created 100s of millions of training data items from billions of human judgments, working closely with our customers to develop Machine Learning and Data Annotation strategies.
We have San Francisco’s top leadership team in Machine Learning. Our VP of Product ran 1/3 of IBM Watson’s products before she joined us. Our VP of Machine Learning was Chief Scientist at Atlassian (JIRA, Confluence, etc) before she joined us. Our CTO is a former disaster response leader and Stanford PhD. Across the company, we celebrate our diversity and enjoy collaborating within the company and across our customers.
At Figure Eight (formerly CrowdFlower) you will work on a wide range of applications. If you speak to your phone, personalize your music, shop online, or if your car parks itself, then you have likely used machine learning powered by Figure Eight. We also run the world’s largest marketplace for human annotation, with more than 100,000 people regularly working on our platform to provide human training & evaluation for machine learning.
Our technology stack is increasingly using kubernetes and related container technologies, as we provide a seamless experience to customers using one of the cloud providers or running on-premise. We are leading the way in developing ways to serve and integrate machine learning systems with human interaction at scale.
The Ideal Candidate:
This role is ideal for an experienced DevOps Engineer that wants to expand their Machine Learning knowledge, as you will be supporting the full pipeline of Machine Learning. Our SaaS platform consists of annotation software used by 100,000s of people creating training data; Machine Learning models for real-time predictions as scale; and the combination of the two for smart combinations of Human and Machine Intelligence.
You will support every part of this pipeline. You don’t need to have a background in Machine Learning, but you do need to have an interest in expanding your knowledge of Machine Learning services. Within the company, we train all our engineers on Machine Learning, and how to deploy them on services including AWS, Google Cloud, and Microsoft Azure, so you will have the opportunity to learn all of these technologies alongside the other team members, and you will lead DevOps strategies to support them.
- Work closely with Application Developers and enjoy participating in the architectural discussions.
- Work closely with Machine Learning Scientists and enjoy thinking about making services scale.
- Support of production infrastructure and services, including our
- AWS Infrastructure such as EC2, S3, IAM, Route53, Elasticache, Load Balancers, CloudWatch etc.
- Java and Rails Applications
- Docker and Kubernetes
- PostgreSQL and Redis databases
- Provide leadership to the team in mastering technologies, identifying and implementing worthwhile new technologies and improving our process.
- Continuous delivery (CI/CD) using Jenkins, Maven, Artifactory, Docker, Chef/Ansible.
- Site reliability and availability, including end-to-end performance, service monitoring, alerting, capacity sizing and planning.
- 24/7 on-call rotation for production support, troubleshooting production and development issues. After-hour emergencies are rare, and you will help us make them even rarer!
- Business continuity planning and testing.
Skills and Experience:
You must have
- At least 5 years of DevOps and system administration experience, preferably in mid or late startups.
- At least 3 years in managing AWS or GCP cloud infrastructure.
- Experience in configuring and supporting SaaS environments, provisioning resources, monitoring utilization and making adjustments in accordance with SOPs
- Expertise in Docker. Kubernetes would be an added advantage.
- Experience monitoring/APM tools such as New Relic, CloudWatch, PaperTrail and Rollbar.
- Linux administration (Ubuntu, AWS AMI) and scripting (e.g. shell script, Python).
- Soft skills, e.g. team player, clear and concise communication, problem solver, sensor of humor.
Desired skills, but not mandatory:
- Expertise in database scalability and availability, preferably with PostgreSQL and Redis.
- Building hybrid cloud using VMWare, CloudFoundry.
- Experience in building PaaS (e.g. Heroku, RedisGreen, Deis).
- Data protection and secret handling technology such as Vault.
- Logging (e.g. splunk, Graylog) and Elastic Search (ELK).
- Managing micro-services and real-time event processing is a big plus.