ML Engineer

Overview

Notable is the leading intelligent automation company for healthcare. Customers use Notable to drive patient acquisition, retention, and reimbursement, scaling growth without hiring more staff. We don’t just make software. We are on a mission to fix the broken U.S. healthcare system by helping to eliminate the massive administrative burden that is placed on our nation’s healthcare staff. We hire people from diverse backgrounds and are always looking for employees who bring fresh ideas to our space. Passion is paramount, and at Notable, you will get to work with other talented people who aim to set the new standard for innovation in healthcare.

As an ML/AI engineer at Notable, you’ll work on developing and deploying conversational AI models and agents across text, image and voice modalities, helping users automate tedious but valuable healthcare administrative tasks.

We look for smart people who can implement well crafted solutions to complex problems in a fast paced environment, and who can help us attract more smart people.  We don’t expect you to have experience with our stack (Google Cloud Platform, Python, Tensorflow, Kubeflow, PyTorch, FastAPI, Kubernetes), but we do look for demonstrated mastery of your chosen development stack and a desire to learn new technologies.

Day-to-day responsibilities:

• Work with the product development team and product managers to define scope of work, timeline and product specifications

• Work with backend engineers to deploy, maintain and scale AI models

• Define interfaces between the microservices that runs and delivers AI models

• Discover, collect, clean and transfer data to train AI models

• Experimentation of different AI models, methodologies, frameworks and communicate critical evaluation metrics to product teams.

• Explore, refine, improve best practices within the ML team

• Push the boundaries of ML and AI and innovate on how to best leverage existing technologies to solve new problems

Experience: 

• 7+ years of experience working in a relevant role

• Demonstrated ability to translate business requirements and metrics into machine learning model specifications

• Quickly prototype new models from open-sourced code and demonstrate results

• Ability to design and train new model architectures for complex data

• Experience working with real-world data: large, messy, incomplete, irregular, etc.

• Experience working with a mix of structured and unstructured data

• Proficiency with Python and the standard ML stack (numpy, pandas, scikit-learn)

• Experience with a deep learning package, e.g. Tensorflow, PyTorch

• Experience deploying ML models in production

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