Senior AI Engineer I
Job description
About the role
You will architect and ship LLM-powered applications that directly enhance commercial, billing, and customer experience workflows. This role demands a high degree of ownership where you design, prototype, and iterate on automation solutions that move the needle on operational efficiency. You will translate ambiguous business requirements into concrete technical specifications alongside cross-functional partners. Your work will center on building scalable AI systems that are reliable, explainable, and responsible. You will be instrumental in shaping the vision and culture of this new team from its inception. You will help define best practices that establish the standard for how AI is implemented across the organization. This is a role where your technical decisions have immediate and significant business impact.
Key facts
What you'll do
Design and deploy LLM-powered applications including retrieval systems, AI agents, workflow automation, and vector storage that integrate with internal systems and data sources.
Partner with business stakeholders in Sales, Marketing, Revenue Operations, Billing, Customer Experience, and IT to identify automation opportunities, scope requirements, and define success metrics.
Build lightweight data pipelines and analytics to support AI applications and rigorously measure their impact on operational efficiency.
Own projects end-to-end, navigating ambiguity and transforming ideas from prototyping and iteration through robust production deployment and monitoring.
Establish best practices for prompt engineering, evaluation methodologies, and responsible AI use as the team scales and processes evolve.
Develop and lead internal training sessions on LLM fundamentals, prompt engineering, and AI-assisted development tools to elevate the entire organization.
Collaborate closely with data scientists and product managers to iterate on models and features, ensuring solutions are practical and aligned with commercial objectives.
Leverage cloud infrastructure on AWS to ensure solutions are secure, scalable, and maintain high standards of performance and reliability.
Requirements
5+ years of professional experience in software engineering, data science, or ML engineering with a proven track record of delivering production-grade systems.
Hands-on experience building applications with LLM APIs from providers such as OpenAI and Anthropic, demonstrating fluency in integrating these models into functional products.
Strong proficiency in Python and comfort with SQL and relational databases to effectively manipulate, query, and manage data.
Experience deploying and maintaining production systems, including containerization, orchestration, and monitoring in cloud environments.
Familiarity with AWS services and toolchain is essential for building and operating scalable solutions.
Excellent communication skills to translate complex technical concepts into clear business recommendations and vice versa.
Self-directed mindset capable of thriving in ambiguity and driving initiative in a fast-paced, 0-to-1 environment.
A bias toward action where you prioritize execution, learn rapidly, and iterate based on real-world feedback.
Nice to have
Previous entrepreneurial experience that demonstrates comfort with building ventures from the ground up.
Experience with retrieval architectures, vector databases, and embedding models to build intelligent information retrieval systems.
Familiarity with agent frameworks or workflow orchestration tools to coordinate complex multi-step processes.
Experience with traditional ML frameworks such as scikit-learn, PyTorch, or TensorFlow for developing predictive models.
Prior work in healthcare, biotech, or other regulated industries where compliance and data sensitivity are paramount.
Practical notes
This is a hybrid role requiring 2-3 days per week on-site in Menlo Park with the team.
The position is based in the United States, and candidates must be eligible to work in the country without sponsorship at this time.
Applications will be reviewed on a rolling basis until the role is filled, so early submission is encouraged.
Candidates should be prepared to discuss their technical portfolio, including examples of deployed systems and contributions to production AI initiatives.
Interviews will likely involve technical assessments focusing on system design, coding, and practical problem-solving related to LLM applications.
Successful candidates will demonstrate not only technical excellence but also alignment with the company's mission of transforming healthcare through transparent and collaborative innovation.