AI/ML Job: Full Stack Machine Learning Engineer

Full Stack Machine Learning Engineer at Roboflow

Remote › 🇺🇸 100% remote position (in the US)   (Posted Jun 24 2022)

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Job description
Roboflow is hiring our third full-time engineer on the machine learning team to contribute to the core machine learning tools and infrastructure that underpin the Roboflow platform. This role will inevitably involve wearing a lot of hats. Wide-ranging curiosity and enthusiasm for diving into abstract problems, coming up with good solutions, and seeing them through to completion are essential responsibilities.

Our core belief is that computer vision is a foundational technology that is going to transform nearly every industry. This is an opportunity to shape how millions of developers will experience and use it for the first time. Your contribution will have a massive impact.

We anticipate the role being focused 60% on backend, 20% on machine learning, and 20% on other areas. You certainly don't need to be an expert in all of these areas; but should be excited to learn new skill sets as you need them. We also hope you'll bring some new knowledge and experiences you can share to help level-up the rest of the team. The above tools span code in our machine learning stack to date - your opinions on new technologies that we adapt for new tasks will be highly valued.

What We Need From You

On the machine learning team, we primarily work on building and maintaining technology within Roboflow’s training, search and deployment services, but from time to time we're also helping deliver on enterprise contracts, and coding awesome open source projects and sample projects.

In the beginning, you will be tackling projects in close collaboration with your fellow machine learning and product team members. As you progress in your knowledge of Roboflow’s mission and tools, you will have a wide degree of freedom to advocate for and drive your own projects. If you need a rigid list of tasks spelled out in a multi-month roadmap, this role probably won't be a good fit.

We’re especially keen to add some rigor to our processes and build the foundation for scaling the engineering organization and the machine learning team.

Technology

Our goal is to build the world's best computer vision infrastructure so our users don't have to. This means we handle a lot of challenging complexities like seamlessly ingesting dozens of data formats, processing millions of images per day, and deploying auto-scaling machine learning infrastructure that can handle our customers' most demanding training and deployment needs.

Our core app sits atop Firebase with assistance from auto-scaling groups of Docker containers (for jobs like archiving datasets and training models). We also heavily lean on serverless infrastructure so we can gracefully deal with bursty traffic involved in manipulating datasets that can range anywhere from one hundred to one million images.

Our machine learning infrastructure runs in AWS, with a few deployments spanning into GCP. We train and deploy various state of the art models in a variety of machine learning frameworks. All of our machine learning applications are closely integrated with the core Roboflow web application.

We also maintain a library of Colab notebooks our customers can use to train common computer vision models, a directory of public datasets, and a web of format specifications. We see building and supporting mini-projects like these that are helpful to the community at large as part of our role in democratizing computer vision.

Skills
Backend: node, Docker, python, Flask, pip, REST
Machine Learning: PyTorch, TensorFlow, ONNX, OpenVINO, TensorRT, TFjs
Mobile: Swift iOS, Kotlin/Java Android
AWS: EC2, ECR, ECS, Lambda, Cloud Watch, Batch, S3
Google Cloud: Cloud Run, Cloud Functions, Cloud Storage, PubSub, GCE
Frontend: Firebase, React, jQuery, HTML, CSS

Example Projects
Building the conversion scripts, inference scripts, and SDK for mobile deployment
Implementing more robust inference usage tracking, reporting, and analytics
Expanding the capabilities and scope of the Roboflow REST API and PIP Package
Building a dashboard where users can track their model deployments
Ensuring uptime of training and inference infrastructure
Creating a multi-region compute waterfall in AWS to ensure the highest level of GPU availability for our customers wishing to train
Sharding our inference functions to serve an ever growing user base

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