Machine Learning Engineer at Roboflow
About the role
Roboflow is hiring a second full-time engineer for our machine learning team to contribute to the core machine learning tools and infrastructure that underpin the Roboflow platform. As part of our growing machine learning team, you will have the opportunity to push core advancements at Roboflow that will be used by millions of developers.
As an integral part of our early team, 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.
Skills - you should be familiar with many of these concepts and technologies and have built projects with some of them:
- PyTorch, TensorFlow, ONNX, OpenVINO, TensorRT
- Docker, Python, Flask, PIP
- AWS - EC2, ECR, ECS, Lambda, Cloud Watch, Batch, S3
- PIL, Darknet, OAK, CLIP, OpenCV
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.
What We Need from You
On the machine learning team, we primarily work on building and maintaining technology within Roboflow’s training 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.
To give you an idea of what it will be like to work here, here is a sampling of a few projects you might work on in your first few months:
- Extending our deployment options to TFLite and CoreML and integrating these conversions into our training routines
- Keeping an eye on our library of Colab notebooks and QA’ing them for functionality
- Experimenting with and recommending new machine learning models
- Ensuring uptime of training and inference infrastructure
- Integrating semantic segmentation models into our backend training and deployment
- 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
- Implementing more robust inference usage tracking, reporting, and analytics
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.
Why you should join Roboflow
We're on a mission to remove barriers that prevent developers from building their own computer vision applications. Roboflow streamlines the process of labeling, training, and deploying a computer vision model.
Computer vision is going to transform every industry. We're already seeing this play out in fields like transportation (self driving cars), agriculture (drone spraying), and medicine (early stage cancer detection). But these superpowers shouldn't be locked up in the handful of giant technology companies that can afford to hire teams of machine learning PhDs.
Roboflow enables any developer to use computer vision without being a machine learning expert. Our product is the key missing infrastructure that allows developers turn raw images into a useful model -- replacing a sprawling list of one-off utils everyone previously had to reinvent and enabling our users to have working models in hours, not weeks.
For example, Sarah Hinkley from Barn Owl Drones uses vision to identify weeds from crops in drone images so her customers can use fewer herbicides and grow more. She's one of our over 50,000 users working on problems we couldn't even imagine when we got started!
Today, Roboflow has eight full-time team members spread across the United States. Their roles range from machine learning to sales. We also have a high school software development intern, and a part-time employee researching our new signups. Kelo, Amanda's dog, is the best at frisbee among us.
We're united in our common goals to create high quality products and place our users first. Since we're a small upstart, that means building things really quickly and fixing bugs right away. As with any rapidly scaling startup, we hope to build a team that is both versatile and adaptable. This role has tremendous potential for growth. As such, we believe that coachability and enthusiasm are more important than experience or qualifications. If you’re excited about this opportunity, we want to hear from you.
We strongly encourage applicants from backgrounds that are traditionally under-represented in tech to apply - especially those who identify as Black, Latinx, Native American, Asian and/or LGBTQ+. People who identify as part of these groups have also been under-represented at Roboflow, but intentionally recruiting a team with unique backgrounds is one of several ways we are working to add more distinct viewpoints to our company.
Roboflow went from zero to over 20,000 users in 2020 (and now to over 50,000 in 2021) and our customers are requesting features and product enhancements faster than we can provide them.
We're starting to build out our engineering, marketing, and sales teams. As an integral part of our core team, all roles will inevitably involve wearing a lot of hats; we're specifically looking for people excited about learning new things and filling gaps where needed. And most importantly, we're looking for people who ship.
Check out our Careers page for more info on the company, how we work together, and how we're building strong culture and camaraderie in a post-COVID world.