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Abridge is an AI-powered healthcare platform founded in 2018 that transforms patient-clinician conversations into structured clinical notes in real-time. The company uses generative AI and Linked Evidence technology to help providers trust and verify AI-generated summaries, setting industry standards for responsible AI deployment in healthcare.
You will join the Data Engineering team as a Senior Software Engineer focused on building and optimizing large-scale data infrastructure that drives business decisions and machine learning research. This is a crucial role in a fast-growing, mission-driven startup processing millions of medical conversations monthly.
Key responsibilities:
- Build and maintain scalable data services, pipelines, and storage solutions for unstructured application data used in ML training and evaluation
- Build and manage OLAP databases, ETLs, and general data tooling for analytics, business decisions, and product features
- Work closely with frontend/backend engineers, product managers, and analysts
- Optimize data infrastructure for throughput, latency, and reliability
- Investigate and resolve issues identified through data operations monitoring
- Design data integrations and data quality frameworks
Abridge has offices in San Francisco (Mission District), New York (SoHo), and Pittsburgh (East Liberty). The company culture emphasizes extreme ownership, empathy-driven decision-making, and supporting clinician and patient needs. Benefits include generous PTO, comprehensive health coverage, HSA contributions, paid parental leave, 401(k) matching, equity grants, lifestyle wallet, mental health support, and sabbatical leave after 5 years.
Requirements:
- 8+ years of experience in Data Engineering or Backend Engineering with focus on data systems
- Proficiency in at least one general-purpose programming language (Python, Java, Scala) and SQL
- Experience with at least one modern cloud provider (GCP, AWS, Azure) and accompanying data services
- Experience building systems that manage ingest, transformation, and management of structured and unstructured data
- Deep knowledge of modern data infrastructure best practices
- Experience with distributed systems and distributed processing frameworks
- Experience with Terraform, Kubernetes, and containerization technologies
- Ability to prioritize amidst changing priorities in a fast-moving environment
- Bonus: familiarity with deploying ML models at scale; experience building well-modeled, documented, maintainable data products
