MLabs is building a Clinical Data Intelligence platform to automate complex, low-level cognitive tasks involving clinical data and knowledge, aiming to dramatically increase efficiency in healthcare.
Requirements
- 7+ years of full-time (not internships or contract work) experience in AI/ML, Data, or Full Stack Software Engineering with data-intensive systems.
- Strong background in software engineering, AI/ML/NLP engineering, and data analytics/engineering.
- Experience developing and deploying code in environments with high standards for security, privacy, and compliance (e.g., familiar with HIPAA and SOC 2 requirements).
- Highly proficient in Python and SQL, and experience with cloud environments (e.g., GCP) and data tools (e.g., BigQuery, dbt, Pandas).
- Experience with LLM prompt engineering and machine reading/knowledge graph technologies.
Responsibilities
- Build and improve core AI/ML systems for extracting and summarizing clinical and health information that directly power applications used by clinicians daily.
- Engineer and analyze data—including scraping, collecting, transforming, querying, mining, and distilling structured/unstructured data—to evaluate system quality, draw insights, and expand/refine our clinical knowledge graph.
- Develop and deploy secure code, maintaining strict confidentiality, integrity, and availability of information systems in compliance with HIPAA and SOC 2.
- Build and improve supporting data/AI/ML systems and automations, ensuring seamless integration and scaling.
- Participate in rotating on-call duties to ensure high system uptime and reliability.
- Work across a modern, robust tech stack including Python, SQL, PostgreSQL, PyTorch, LLM Prompt Engineering, and Google Cloud Platform (GCP).
Other
- Must be authorized to work in the United States and capable of working during core hours in the Pacific timezone.
- H1B transfers are available, but we cannot offer new H1B sponsorships at this time.
- Familiarity with the healthcare and medical domains.
- Remote-first company culture, with relocation encouraged to the San Francisco Bay Area (Hybrid option available, meeting once a week or more).
- Exceptional candidates may be considered for fully remote positions.
