Hoodline is creating the nearby button for the internet by analyzing, organizing and distributing the world’s content. We’re figuring out how cities really work today, then making predictions about how they’ll look in the future.
To do this we’re looking for a Senior Machine Learning Engineer who can help us dig into a variety of textual data that we are getting access to. We currently offer a local content platform
that provides relevant nearby articles, photos and videos for any app or site. To distribute all this content, we tag each story with a context taxonomy of more than 20 categories using machine learning, allowing people to receive relevant information about anything from where to eat to where to move.
We partner with 200+ publishers, including ABC television, McClatchy, Advance Digital, TripAdvisor and Vice, and do our own reporting to discover insights for every location. We serve this information to partner's apps and sites including Uber, Eventbrite and Yelp.
Come help us figure out how cities really work.
About the position:
We have a flat engineering organization but as an founding member of our Machine Learning team this role will have an outsized impact on future of Hoodline’s platform.
Your primary responsibility will be developing Machine Learning models for our platform and run them at scale to automatically tag incoming partner data. This will involve essentially turn terabytes of incoming and proprietary local data into insights, to understand how cities really work. You will need to be an expert in Natural language processing (parsing, entity recognition and detection, text classification etc.), and language modeling at scale using neural networks and classical techniques.
At the stage we are in, it’s a chance to have your fingerprints all over the product, work with and help build a world class team working one of the largest and most challenging problems—local. If you are curious, driven and have a bias for speed and choosing the harder path, this could be the right home for you. We’re going all out!
Our team has years of success in consumer tech, online and local media. We’re backed by a range of top tech investors including Rakuten, Greylock Partners, Social Capital, Graph Ventures, Charles River Ventures, Eric Schmidt's Innovation Endeavors, Pear Ventures, Matter Ventures, John S. & James L. Knight Foundation, 500 Startups, and SoftTech VC. Angel investors include Joi Ito, Director of the MIT Media Lab, Cyan and Scott Banister, Ben Silbermann of Pinterest and Shane Smith of VICE.
- Leveraging our large amount of textual data in various form to build and train natural language understanding systems
- Developing new algorithms and modeling techniques, conducting experiments to prove these new techniques and integrating them into the live production system for tagging, text classification, sentiment analysis, named entity recognition etc.
- Keeping up with recent advances in natural language processing, machine-learning and big data processing
- Work closely with other team members on the development and support of new products
- Run online experiments and develop metrics that can drive product requirements
- MSc (PhD preferred) in computer science and, natural language processing, machine learning
- Experience with commonly available tools and infrastructures for natural language processing, text mining, machine learning and parallel data processing
- Experience in large scale software development and product development process (code design, test plan and code reviews)
- Knowledge of one modern programming language (C++, Java, Scala) and scripting language (Python, Perl)
- Experience working with large amounts of user generated content and process data in large-scale environments using Amazon EC2, Storm, Hadoop and Spark
- 3-5 years of professional experience in the field
- Passionate about data driven products
- Curiosity about the local content and local news space
- Is an active participant in meet-ups / groups related to: Data Analytics, Hadoop, Cloud Computing, Data Visualization, Data Mining, MapReduce, Machine Learning, High Scalability Computing, Predictive Analytics