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Clarifai // advanced image recognition using neural networks
 
Engineering, Full Time    Tallin, Estonia    Posted: Saturday, September 25, 2021
 
   
 
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JOB DETAILS
 

About the Company:

Clarifai is a leading, full-lifecycle deep learning AI platform for computer vision & natural language processing. We help organizations transform unstructured images, video, & text data into structured data at a significantly faster & more accurate rate than humans would be able to do on their own. Founded in 2013 by Matt Zeiler, Ph.D. Clarifai has been a market leader in AI since winning the top five places in image classification at the 2013 ImageNet Challenge. Clarifai continues to grow with employees remotely based throughout the United States & in Tallinn, Estonia.

We have secured $40M in funding up to date, backed by Menlo Ventures, Google Ventures, USV, NVIDIA, Qualcomm, Osage, Lux Capital, LDV Capital, & Corazon Capital.  

Clarifai is proud to be an equal opportunity workplace dedicated to pursuing, hiring, & retaining a diverse workforce.

The Opportunity:

As a Senior Engineer, you collaborate with your colleagues on the backend team to set a technical vision for our AI systems, as well as train & mentor engineers to develop their skills & technical understanding. You architect our AI web services in addition to improving existing features, reliability, flexibility, & scalability as usage increases.

We are looking for someone comfortable in several programming languages & excited about building new features in Go & Python. You should care about software design & have built systems that other people love to use & work with, & have experience building & scaling distributed, highly-available systems.

Impact:

You build the systems & services behind the Clarifai magic. Neural networks are data-hungry beasts, & you keep them well fed! 

As a core team member building & improving our AI platform you will work with a variety of technologies (Python, Golang, C++, Postgres, Redshift, Docker, Redis, REST, AWS, & Kubernetes. Don't worry if you haven't used some of these, we will teach you anything you don't know!).  Your work will enable customers & researchers to build production level ML/DL models at scale.

Requirements:

  • A minimum of 3+ year of backend software development experience (i.e. C++, Python, Java, Golang, C#, etc.)
  • Understanding of microservices architecture & best practices
  • Strong background in building scalable & fault-tolerant distributed systems. 
  • Past experience in building applications such as data pipelines, data caching/storage systems, and/or RPC services
  • Experience with performance tuning 
  • Self driven, communicative, & fun to work with
  • Agile experience

Great to Have:

  • Experience with Golang or Python is a huge plus
  • Previous experience with developing machine learning infrastructure
  • Deep understanding of any SQL/NoSQL DB. 
  • Experience working in a tech company/industry. AI experience is a plus.
  • Experience working on a distributed team

Objectives:

In the first month, youll start off by learning the ropes. You will:

  • Get familiar with our code base (as well as the backend & infrastructure teams). We would like you to take this time to get comfortable working with what weve built & who has helped build it so far, & give us the feedback only a fresh perspective can bring.
  • Learn about the distinctive challenges of machine learning systems using GPUs.
  • Identify & resolve production bugs.

Within 3 months, you will have gained confidence in the code & will:

  • Help to plan feature development, requirements, & our technical road map.
  • Accelerate development of our machine learning API feature set.
  • Improve user management & refining API permissions
  • Build & measure benchmarking & stress test tools.

Within 6 months, youll:

  • Measure & optimize the customer facing custom training API service.
  • Design, deploy & run web-scale distributed storage systems of various flavors, both relational (mysql, postgres) & nosql (redis, elasticsearch, etc.).
  • Expand on quality assurance infrastructure & continuous deployment.
  • Identify web security risks & write tools to improve security issues.
  • Work closely & communicate with product managers on hiring & timelines.
 
 
 
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