Hive is looking for Staff Machine Learning Engineers to help them stay at the forefront of deep learning technology, prototype state-of-the-art neural net models, and launch these models into production. They need engineers who can handle terabyte-scale datasets, learn new technologies, and move fast to take ownership of projects.
Requirements
- You have successfully implemented highly-available distributed systems/microservices
- You have delivered scalable backend APIs
- You have experience writing code and training across distributed systems
- You are an expert in machine learning frameworks, such as PyTorch or Tensorflow
- You are an expert in scripting languages such as Python and/or shell scripts, particularly for data analysis
- You are a subject matter expert in at least one focus area of machine learning, such as computer vision or natural language processing
- You can lead end to end development of new products
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
Other
- You have 8+ years of experience building web applications
- You have strong interpersonal and communication skills with a bias towards action
- You have the ability to understand and make well-reasoned tradeoffs in designing features
- Please reach out if you are interested in joining the future of AI!
- Work Location: In person
