Abridge's AI-powered platform needs to improve clinical documentation efficiencies and enable clinicians to focus on patients by transforming patient-clinician conversations into structured clinical notes in real-time. The company is looking to scale its AI-driven solutions and optimize the core infrastructure that powers its machine learning models.
Requirements
- Expertise with ML toolchains such as PyTorch, Tensorflow or distributed training and inference libraries.
- Experience in building and deploying machine learning models in production environments.
Responsibilities
- Collaborate with ML and product teams to scale backend infrastructure for AI-driven products, focusing on model deployment, throughout optimization, and compute efficiency.
- Design, deploy and maintain scalable Kubernetes clusters for AI model inference and training
- Develop, optimize, and maintain ML model serving and training infrastructure, ensuring high-performance and low-latency.
- Collaborate across our cross-functional product teams, working closely with design, product, and infrastructure to ship features to our customers.
- Receive mentorship from our experienced machine learning and software engineering teams, while collaborating one-on-one with teammates within your pod.
Other
- Currently pursuing a Bachelor's or Master's degree in Computer Science, Software Engineering, Machine Learning, or a related technical field
- Expected graduation date in Spring 2026
- At least one prior software engineering internship or co-op experience.
- Passion for and understanding of Abridge’s mission.
- A curious, adaptable, and proactive mindset, with a desire to learn and grow in a fast-paced startup environment.
- A willingness to pitch in wherever needed—we move fast, and we need team players who are eager to contribute.
- Must be willing to work from our San Francisco or New York City office at least 3x per week
