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 businesses can use 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 of any size or budget. That’s why we build products to make it easy, quick, and inexpensive for developers and businesses to innovate with AI, go to market faster, and build better customer experiences.
We have secured a $30M Series B round of funding and are 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.
The AI Deployment unit is an engineering team dedicated to helping Clarifai customers extract maximum value from our platform. As Deployment Engineer, you will bring your technical know-how to the front lines, working directly with customers on deployment, integration, model building custom features and onboarding.
- Deploy cutting edge complex technical solutions to solve our customers’ most challenging problems
- You will build and maintain intuitive web applications that will give users hands-on access to our machine learning platform and custom training, which allows users to train their own models without using any code.
- You will develop reusable modules, components, and build tools for both internal and external use cases.
- You will work with data annotation teams to wrangle data and generate cutting edge models, sometimes on the Company platform but sometimes in code.
What Skills You Bring
- A minimum of 2 years of professional ML development experience
- Experience working directly with clients and customer
- Experience with machine learning or data science experiments
- Strong Python (or other equivalent language)
- Experience fielding/debugging complex systems - i.e. why is a model returning results that are unexpected?