Software Engineer, AI/ML

Overview Verantos (https://verantos.com) is a market leader in high-accuracy real-world evidence (RWE) generation. The Verantos RWE platform integrates heterogeneous real-world data sources and generates evidence with the accuracy necessary for regulatory and reimbursement use. The Verantos RWE platform leverages data science and artificial intelligence, along with advanced data sources such as electronic health records (EHR), to generate RWE capable of supporting complex clinical studies. Some of the largest biopharma companies in the world are Verantos customers. We use a heterogeneous tech stack on AWS with data processing, artificial intelligence, workflow, and analytic components. Job description We’re looking for a Software Engineer with a solid foundation in Python and an interest in working on data-driven and AI-first systems. You’ll contribute to building and maintaining backend systems that support our AI workflows and real-world data processing. Responsibilities Contribute to the development of Python-based applications that support AI/ML pipelines Assist in data ingestion, transformation, and integration from real-world healthcare data sources Support AI model deployment and monitoring as part of production workflows Collaborate with data scientists and senior engineers to enable novel AI-based agents and other features Write clean, efficient, and testable code Learn and apply cloud tools (like AWS) to support scalable solutions Qualifications Bachelor's degree in Computer Science, Engineering, or a related field 2+ years of experience in software engineering Strong Python skills and familiarity with software development best practices Exposure to machine learning concepts (e.g., LLMs, model training, APIs, or inference) Basic experience with cloud platforms (e.g., AWS, GCP) Understanding of databases, REST APIs, and Git A strong interest in AI/ML and healthcare technology Nice to have Familiarity with frameworks like scikit-learn, TensorFlow, or PyTorch Experience with tools like Docker, Jupyter notebooks Knowledge of data processing libraries such as Pandas or NumPy Experience leveraging LLM-enabled development tools such as Cursor, Windsurf Familiarity with OMOP (Observational Medical Outcomes Partnership) data models and EHR (Electronic Health Record) data models is a plus.