Backend Engineer

Scale AI

San Francisco, CA, US
  • Job Type: Full-Time
  • Function: Engineering Software
  • Post Date: 12/30/2020
  • Website: scale.com
  • Company Address: 303 2nd St, South Tower, 5th FL, San Francisco, CA, 94107, US

About Scale AI

Scale accelerates the development of AI applications by helping machine learning teams generate high-quality ground truth data. Our advanced LiDAR, image, video and NLP annotation APIs allow machine learning teams at companies like OpenAI, Lyft, Pinterest, and Airbnb focus on building differentiated models vs. labeling data.

Job Description

Scale is building out one of the largest hybrid human-machine systems. Scale’s self-regulating system automatically trains workers and ensures continuous quality and optimal allocation. We have thousands of human labelers that complete millions of tasks a month, and that comes with a host of interesting technical challenges. From product to systems to infrastructure engineering, we’re tackling it all to accelerate the development of AI.
 
The Infrastructure team is responsible for building the core abstractions and infrastructure on which the products can be built and iterated rapidly. The team owns how data flows throughout the scale platform. We’re looking for people with a strong background or interest in building distributed systems, data-intensive applications, and machine learning infrastructure. You have a growth mindset and are comfortable learning new technologies.

You will be:

  • Creating, building, educating, training and designing cloud computing architectures for our customers and internal teams
  • Work directly with our engineering and sales teams to create backend and infrastructure solutions to meet their challenging data and security needs.
  • Create abstractions of our core infrastructure which can scale to millions of humans and ML models working together.
  • Propose, design, build, and deploy security improvements across Scale’s environments.
  • Work with our advisors and third party vendors and auditors on security compliance, pen tests and mitigations.
  • Build systems capable of handling millions of frames of data every day, making it available to both our workforce and our internal teams with high availability.

This role could be a fit if you have:

  • 2-7 years of industry experience as a software engineer post graduation
  • Systems engineering experience with real-time and distributed system architecture.
  • Experience building systems that process large volumes of data.
  • Experience or interest in using the following: AWS, Typescript, Node, Mongo, MLflow, Spark, Presto, Python (note that we are mostly language-agnostic and are open to using whatever is the best tech for the problem at hand)
  • At least a Bachelor’s degree (or equivalent) in a relevant field.

Nice to haves:

  • Prior startup experience to help us grow responsibly
  • Experience with core AWS technologies such as VPC, EC2, ALB, ASG, Spot Instances
  • Experience in operating or managing Infrastructure such as Spark, Presto, Hive
  • Experience working with Docker, Kubernetes, and Infrastructure as code (eg terraform); especially for running GPU/ML workloads
  • Mentored and grown members of your team or been a tech lead on large projects
 
About Us:
At Scale, our mission is to accelerate the development of Machine Learning and AI applications across multiple markets. Our first product is a suite of APIs that allow AI teams to generate high-quality ground truth data. Our customers include OpenAI, Zoox, Lyft, Pinterest, Airbnb, nuTonomy, and many more.
 
Scale AI is an equal opportunity employer. We aim for every person at Scale to feel like they matter, belong, and can be their authentic selves so they can do their best work. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

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Disclaimer: Local Candidates Only
This company does NOT accept candidates from outside recruiting firms. Agency contacts are not welcome.