COMPANY SUMMARY

Sorcero’s medically-tuned intelligence platform transforms life sciences decision-making, accelerating patient access to life-saving treatments. By harnessing insights from global medical data, we empower industry leaders to deliver breakthrough therapies. Founded in 2018 by pioneers in AI, public health, and personalized content, Sorcero is a Certified B Corp headquartered in Washington, DC. Our innovation has been recognized by over a dozen awards, including Fast Company’s Most Innovative companies of 2024, and six foundational medical AI patents. For more information, visit www.Sorcero.com.

REQUIRED QUALIFICATIONS:

• 3+ years of enterprise SaaS software development experience
• 3+ years of Python experience
• 3+ years of experience with cloud computing (GCP preferred)
• Experience with Large Language Models
• Experience with common Data Science tooling and packages
• A positive and collaborative attitude
• Initiative and self-motivation
• Motivation and drive to continuously improve and learn by trying new tools, processes, or methodologies
• Excellent verbal and written communication skills

THE ROLE

We are looking for an AI Engineer with experience in enterprise SaaS environments, with a focus on Generative AI and Data Science skills. The ideal candidate will have experience designing, building, and training AI-powered services for processing complex data in the life sciences domain.

RESPONSIBILITIES

• Explainable AI Development: Build AI systems with transparency and explainability at their core, allowing users to understand how and why specific conclusions are reached—critical for life sciences applications where trustworthiness is paramount
• Model Development & Refinement: Collaborate with the AI and MLOps teams to build and prototype new solutions for our customers, as well as our enterprise SaaS product offerings
• Cross-Functional Collaboration: Work with product and delivery teams to assist with the design and development of AI-powered features in our products
• Model Creation & Training: Create, train and test models that deliver our AI capabilities with a focus on scientific accuracy and domain relevance
• Model Optimization: Fine-tune, optimize and ground models to ensure they are sound and fit for purpose. Own assignments from proof-of-concept to design, and code delivery to MLOps
• Product Understanding: Understand our products from both the customer perspective and the engineering perspective
• AI Advocacy: Actively advocate for improving AI best practices, both internally in the tools and services offered by the AI team, as well as externally in the way the product teams integrate against those services

Success in this role will be measured by:

• Ability to develop accurate, scalable, and explainable AI solutions that directly address customer needs
• Reduction in processing time for complex scientific datasets while maintaining or improving accuracy
• Quality of integration between AI systems and product features as measured by customer satisfaction
• Contributions to our AI toolkit that enable other teams to build more efficiently
• Demonstrated ability to balance innovation with production stability in deployed AI system