• Define technical strategy across multiple AI initiatives, driving architectural decisions, influencing product and research direction, and aligning engineering investments across teams to maximize long-term business and clinical impact. • Design, build, train, evaluate and improve advanced machine learning and LLM-based systems for patient and provider-facing products (e.g., conversational AI, personalization, user understanding, clinical decision support, chronic care management). • Own problems end-to-end: scope the problem with clinicians and product partners, build datasets and evaluations, iterate on modeling, and ship to production with the right monitoring and guardrails. • Develop robust evaluation frameworks — offline benchmarks, human-in-the-loop review, online experiments — that give us confidence our models are safe, accurate, and improving over time. • Build and improve the platform that lets the team move quickly: data pipelines, training and inference infrastructure, prompt and model management, and tooling for clinical reviewers. • Partner closely with clinicians, product, and engineering to translate medical and operational requirements into ML problems and ship measurable improvements to patient and clinician experience. • Set technical direction for your area, mentor other engineers, and raise the bar on engineering and scientific rigor. The scope of leadership scales with seniority. • Stay close to the literature and the rapidly evolving AI ecosystem; bring back what is most useful for our patients and our team.

• Lead the technical execution of complex AI initiatives, owning the design and delivery of solutions within a product or technical domain while partnering with senior engineers on broader architectural direction. • Design, build, train, evaluate and improve advanced machine learning and LLM-based systems for patient and provider-facing products (e.g., conversational AI, personalization, user understanding, clinical decision support, chronic care management). • Own problems end-to-end: scope the problem with clinicians and product partners, build datasets and evaluations, iterate on modeling, and ship to production with the right monitoring and guardrails. • Develop robust evaluation frameworks — offline benchmarks, human-in-the-loop review, online experiments — that give us confidence our models are safe, accurate, and improving over time. • Build and improve the platform that lets the team move quickly: data pipelines, training and inference infrastructure, prompt and model management, and tooling for clinical reviewers. • Partner closely with clinicians, product, and engineering to translate medical and operational requirements into ML problems and ship measurable improvements to patient and clinician experience. • Set technical direction for your area, mentor other engineers, and raise the bar on engineering and scientific rigor. The scope of leadership scales with seniority. • Stay close to the literature and the rapidly evolving AI ecosystem; bring back what is most useful for our patients and our team.

Principal Engineer, Artificial Intelligence Authoring Location Boston, Massachusetts Employment Type Full-time Work Arrangement Hybrid (Hybrid-Eligible or On-Site Eligible) - Hybrid: Work remotely up to 2 days per week OR - On-Site: Work 5 days per week on-site Salary $166,640–$250,000 annually Job Description: Position Summary: We are seeking a highly skilled and visionary Principal Engineer, AI Authoring to join our AI Authoring Platform team. Reporting to the Director of AI Authoring Platform, you will lead the design, development, and optimization of Vertex’s AI-driven content generation engine. This role is pivotal in shaping the technical architecture of our platform, ensuring the performance, accuracy, and scalability of our LLM-powered authoring tools. You will tackle complex engineering challenges, including automated document workflows and Retrieval-Augmented Generation (RAG)-based architectures, to support the creation of high-quality, compliant, and efficient regulatory documents. Key Duties and Responsibilities - Design and maintain the end-to-end technical architecture for AI-assisted document generation, focusing on modular LLM integration, prompt engineering, and retrieval pipelines, while partnering closely with the AI Architect to ensure alignment with enterprise standards and scalability. - Build and optimize sophisticated Retrieval-Augmented Generation (RAG) systems that accurately pull from vast clinical and regulatory data sources to assist in document creation. - Engineer the data connectors and transformations required to feed structured and unstructured data into AI models for automated document population. - Develop and implement automated testing and validation frameworks to ensure AI-authored outputs meet the strict safety and compliance standards of the Life Sciences industry. - Rapidly prototype new AI authoring features, such as automated summarization or cross-document consistency checks, to determine technical viability before full-scale development. - Serve as the final escalation point for complex technical blockers, from debugging model hallucinations to optimizing cloud infrastructure for high-compute AI workloads. Knowledge and Skills - Proven experience in designing and implementing AI-driven platforms, particularly those leveraging LLMs and RAG architectures. - Expertise in prompt engineering, retrieval pipelines, and modular AI system design. - Strong background in engineering data workflows, including structured and unstructured data integration. - Experience with automated testing and validation frameworks, particularly in regulated industries. - Proficiency in cloud infrastructure optimization for AI workloads. - Exceptional problem-solving skills, with the ability to address complex technical challenges. - Strong collaboration and communication skills to work effectively with cross-functional teams - Familiarity with the Life Sciences industry and its compliance requirements (preferred). - Experience with automated document workflows and AI-based content generation (preferred). Education and Experience - 8+ years of experience in software engineering, with a focus on AI, machine learning, or technical platform development. - Proven track record of leading the design and implementation of AI-driven systems, including LLM-based platforms and RAG architectures. - Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field. - Hands-on experience with cloud platforms (e.g., AWS, Azure, or GCP) and distributed computing systems. - Demonstrated expertise in building and deploying production-grade AI/ML systems in complex, data-intensive environments. - Experience working in regulated industries, such as Life Sciences, is a strong plus. Why Join Us? At Vertex, you will have the opportunity to work on cutting-edge technologies that make a real difference in people’s lives. We offer a collaborative and innovative work environment, competitive compensation, and opportunities for professional growth. If you are passionate about leveraging AI to revolutionize content generation and thrive in solving complex engineering challenges, we encourage you to apply and join our mission to transform lives through innovation. #LI-HYBRID Pay Range: $166,640 - $250,000 Disclosure Statement: The range provided is based on what we believe is a reasonable estimate for the base salary pay range for this job at the time of posting. This role is eligible for an annual bonus and annual equity awards. Some roles may also be eligible for overtime pay, in accordance with federal and state requirements. Actual base salary pay will be based on a number of factors, including skills, competencies, experience, and other job-related factors permitted by law. At Vertex, our Total Rewards offerings also include inclusive market-leading benefits to meet our employees wherever they are in their career, financial, family and wellbeing journey while providing flexibility and resources to support their growth and aspirations. From medical, dental and vision benefits to generous paid time off (including a week-long company shutdown in the Summer and the Winter), educational assistance programs including student loan repayment, a generous commuting subsidy, matching charitable donations, 401(k) and so much more. Flex Designation: Hybrid-Eligible Or On-Site Eligible Flex Eligibility Status: In this Hybrid-Eligible role, you can choose to be designated as: 1. Hybrid: work remotely up to two days per week; or select 2. On-Site: work five days per week on-site with ad hoc flexibility. Note: The Flex status for this position is subject to Vertex’s Policy on Flex @ Vertex Program and may be changed at any time. #LI-Hybrid Company Information Vertex is a global biotechnology company that invests in scientific innovation. Vertex is committed to equal employment opportunity and non-discrimination for all employees and qualified applicants without regard to a person's race, color, sex, gender identity or expression, age, religion, national origin, ancestry, ethnicity, disability, veteran status, genetic information, sexual orientation, marital status, or any characteristic protected under applicable law. Vertex is an E-Verify Employer in the United States. Vertex will make reasonable accommodations for qualified individuals with known disabilities, in accordance with applicable law. Any applicant requiring an accommodation in connection with the hiring process and/or to perform the essential functions of the position for which the applicant has applied should make a request to the recruiter or hiring manager, or contact Talent Acquisition at ApplicationAssistance@vrtx.com

• Work as a software engineer • Maintain and expand a deep understanding of agentic coding • Design and build agentic coding systems that we can trust to deliver high-quality outputs • Develop a deep understanding of AI application development patterns and techniques, including emerging approaches and architectures. • Take end-to-end ownership of new features, working with other teams to deliver exceptional reliability and a great developer experience. • Work with multiple programming languages: Python and TypeScript, Java, Go. • Serve as a domain expert on AI design patterns, collaborating with field staff to provide best-practices and canonical examples. • Work directly with our developer community to debug issues that need expert attention and get feedback on Temporal features and APIs. • Write public technical documentation describing Temporal concepts and APIs. • Go the extra mile to support a customer in need, on the rare occasion that our teams’ engineering expertise is needed. • Travel to meet your coworkers for a week once or twice a year. • Attend the occasional developer conference to talk about how great Temporal is (optional).