About the Company:
Clarifai is an artificial intelligence company that excels at visual recognition. We do not sell an abstract, futuristic technology - we sell a solution that people can use today to solve real-world problems. We believe that the same AI technology that gives big tech companies a competitive edge should be available to developers and businesses. That’s why we build products to make it easy, quick, and inexpensive for them to innovate with AI, go to market faster, and build better customer experiences. We make “teaching” AI just as accessible as we make using AI, which is why our technology is the most personalized, unbiased, accurate solution in the market.
We have secured $30M in funding up to date, backed by Menlo Ventures, Google Ventures, USV, NVIDIA, Qualcomm, Osage, Lux Capital, LDV Capital, and Corazon Capital. To continue to succeed, we need people like you to join the team!
Clarifai is proud to be an equal opportunity workplace dedicated to pursuing and hiring a diverse workforce.
You’ll join our Field Engineering Team as one of a handful of field engineers, reporting into Scott Foster, the Head of Field Engineering. You’ll work to deliver solutions for the toughest problems defined by our biggest clients. You have the ability to make a significant impact on the company and the AI space as a whole.
The Field Engineering team contributes to Clarifai through collaborations with the rest of Customer Success, Applied Machine Learning, Research, and Backend, as follows:
Field Engineering is wholly responsible for delivery for Clarifai’s most complex Government clients. We marshall the resources of our team, as well as the rest of the organization to ensure we can deliver on-time and with the accuracy and reliability needed in production systems. You will both apply tried-and-true methodologies for building custom machine learning models as well as work with the research and applied machine learning teams to push the envelope of what is possible. You will work with the backend team to deploy new custom APIs and deal with the complexities of on-premise deployment on every type of cloud or device.
You'll be a good fit for Field Engineering if you're passionate about ML, AI, and Computer Vision, are comfortable meeting with clients, and don’t mind travelling a bit to ensure our deployments go off without a hitch.
- Work on Clarifai’s most complex public sector accounts, be part of the team responsible for delivering solutions to their most complex needs
- Lead deployments of our infrastructure to bare metal or any one of the major private or public clouds
- Collaborate with the applied machine learning, research, and backend teams to ensure that our models are production-ready wherever they might be deployed (on-premise in a public cloud, private cloud, IoT, iOS, Android, etc.).
- Train private custom models with curated data and evaluate model performance.
- Support customers once in production with model or infrastructure tweaks, ensure that executives at the client continue to renew.
What You Bring:
- B.S. Computer Science or equivalent
- Python / C++/Go
- Experience integrating complex systems
- Ability to travel up to 25%
At least one of:
- Experience fielding/debugging complex systems
- Kubernetes, Docker, or other containers
- Intermediate-level SQL
- Experience in areas of computer vision such as object recognition, pose estimation, image alignment, optical flow, tracking
You’ll thrive in this role if love to learn fast and move fast.
In your first 30 days, you will:
- Get up to speed on either a customer-facing ML project or an on-premise deployment project
- Learn how we work with the rest of the organization
- Have created your own branch and begun coding
60 days in, you will:
- Be training models for specific client use cases or deploying a new on-premise instance
- Have contributed meaningfully to at least one biweekly sprint
6 months in, you will:
- Be contributing to multiple client projects
- Have contributed to growing our Field Engineering knowledge base, optimized processes, and helped us reduce time-to-value for our customers