About the Role
Our machine learning system is an industry agnostic image that can be quickly spun up to ingest per-project data and run transfer learning at scale given the parameters.
You will be helping us expand our architectural offering and hyperparameters available for users to tune and optimize. In addition, you will be working on Insights and MLOps offering to help users build better, fairer, models.
In addition, you will scout and prototype with state-of-the-art vision algorithms and methods to provide our users and the team with potentially disruptive technologies that can be integrated onto our platform to improve user value.
We are looking for:
- 2-5 years experience developing and integrating machine learning systems
- Experience with stacks that includes PyTorch, Keras, and TensorFlow
- Possess some technical foundation in backend services, querying large amounts of data/ FS, and GCP / Cloud architecture
- Experience building, maintaining, and debugging real-time computer vision systems
- Some experience with container-centric architectures, built with Docker and tools like Kubernetes
- Experience or interest in deep-learning systems, serving APIs leveraging CUDA, GPUs and hardware acceleration, and ML development workflow
- Experience with computer vision applications, writing integration codes, and prototypical notebooks for testing ideas and pipelines
About you
- You are an ML enthusiast that follows State-of-the-Art computer vision publications and repositories. Ideally, you have the ability to write clean, but powerful, Jupyter Notebooks for the team and users to understand various computer vision methods.
- Above all, you have a passion for data, machine learning, and user experience. You understand the tradeoffs with each line of code and have an idea of what the various priorities are with each project.
- Ideally, you have a portfolio of Kaggle notebooks, discussions, or a technical blog that shows your ideas and interests in writing - after all, why wouldn’t you flex your research?