Staff Machine Learning Engineer

About Hive Hive is the leading provider of cloud-based AI solutions to understand, search, and generate content, and is trusted by hundreds of the world's largest and most innovative organizations. The company empowers developers with a portfolio of best-in-class, pre-trained AI models, serving billions of customer API requests every month. Hive also offers turnkey software applications powered by proprietary AI models and datasets, enabling breakthrough use cases across industries. Together, Hive’s solutions are transforming content moderation, brand protection, sponsorship measurement, context-based ad targeting, and more. Hive has raised over $120M in capital from leading investors, including General Catalyst, 8VC, Glynn Capital, Bain & Company, Visa Ventures, and others. We have over 250 employees globally in our San Francisco, Seattle, and Delhi offices. Please reach out if you are interested in joining the future of AI! Staff Machine Learning Engineer In order to execute our vision, we need to grow our team of best-in-class machine learning engineers. We are looking for developers who are excited about staying at the forefront of deep learning technology, prototyping state-of-the-art neural net models and launching these models into production. We value hard workers who have no qualms working with terabyte-scale datasets, who are interested in learning new technologies at all levels of the machine learning stack, and who move fast and take ownership of their projects. Our ideal candidate has experience creating a working machine learning-powered project from the ground up, contributes innovative ideas and ingenious implementations to

the team, and is capable of planning out scalable, maintainable data pipelines. \n

Responsibilities Everything involved in applying a ML model to a production use case, including designing and coding up the neural network, gathering and refining data, training and tuning the model, deploying it at scale with high throughput and uptime, and analyzing the results in the wild in order to continuously update and improve accuracy and speed Write and maintain scalable, performant code that can be shared across platforms Contribute meaningfully to the product and core backend systems by suggesting and executing improvements Improve engineering standards, tooling, and processes Develop novel, accurate, and performant ML algorithms for use at scale Conduct metric-driven research experiments to improve model performance Provide mentorship to and help onboard ML engineers Lead cross-functional collaboration with other teams Contribute to defining strategic direction, planning the roadmap Maintain awareness of industry best practices for data maintenance handling as it relates to your role Adhere to policies, guidelines and procedures pertaining to the protection of information assets Report actual or suspected security and/or policy violations/breaches to an appropriate authority Minimum