All the tools you need to build better vision models, faster
At Encord, we're building the AI infrastructure of the future. Our comprehensive platform includes all the tools developers need to build production-ready AI applications from data curation to annotations and labeling to label validation and model evaluation.
The success of any AI application today relies on the quality of a model's training data — and for 95% of teams, this essential step is both the most costly, and the most time-consuming, in getting their product to market.
As ex-computer scientists, physicists, and quants, we felt first-hand how the lack of tools to prepare quality training data was impeding the progress of building AI. AI today is what the early days of computing or the internet were like, where the potential of the technology is clear, but the tools and processes surrounding it are still primitive, preventing the next generation of applications. This is why we started Encord.
We are working at the cutting edge of computer vision and deep learning, backed by top investors, including CRV and Y Combinator, leading industry executives like Luc Vincent, former VP of AI at Meta, and other top Bay Area leaders in AI. We are one the fastest growing companies in our space, and consistently rated as the best tool in the market by our customers.
What we are looking for
As the first Developer Advocate based in the U.S., you will play a crucial role in shaping our community and establish Encord as a leader in the ML/CV space. Collaborating with cross-functional teams, you will be responsible for educating our community, increasing brand awareness, and establishing Encord’s reputation as leaders in the ML/CV space.
We are looking for smart and ambitious individuals with an established presence in the AI space. We’re still a startup: you’ll have to get hands-on with projects, operate with partial knowledge, and constantly be rethinking how we do things. Plus, move very quickly.
What you will do
In this role, you will:
Generate compelling content (e.g., technical blogs, social media posts, etc.) to educate developers and reinforce Encord’s reputation as leaders in the ML/CV space.
Become a product expert, understand industry use cases, and create technical assets (e.g., product demos, videos, workshops, etc.) to help developers use Encord.
Be a prominent voice in ML/CV social networks (e.g., twitter, slack communities, etc.)
Attend conferences, and host hackathons & webinars to actively engage with the community.
Participate in the AI community in San Francisco and online.
To succeed in this role, you should have:
Professional ML/CV experience & strong technical knowledge of Python, TensorFlow, Pytorch, NumPy, etc.
Excellent technical writing skills with a proven ability to create ML/CV content.
Passion for delivering exceptional products and a deep interest in the technology that drives these experiences.
Ability to simplify complex problems and communicate them effectively to diverse audiences.
Enthusiasm for helping other developers learn and grow.
Strong collaboration and communication skills with a bias for action.
As part of your application, please be sure to include a link to your GitHub and/or personal website so we can get a sense of your coding ability and prior work.
We encourage you to apply even if you do not believe you meet all of the requirements. We are looking for smart talent driven to action more so than accolades.
Competitive salary and equity in a fast-growing startup.
18 days annual leave a year + public holidays.
Fast-paced learning through direct hands-on experience.
Opportunity to reinforce yourself as an AI thought leader.
Encord is a London-based startup building a comprehensive training data solution for computer vision AI applications. We are backed by Y Combinator, the Harvard Management Company, top industry executives, and other leading Bay Area investors.
Started by ex-computer scientists, physicists, and quants, we felt firsthand how the lack of tools to prepare quality training data was impeding the progress of building practical AI. AI feels to us like what the early days of computing or the internet must have felt like, where the potential of the technology is clear, but the tools and processes surrounding it are terrible. We have devised a unique methodology for automating the tasks related to preparing quality training data, in effect turning the training data problem into a data science problem.