Senior Staff Engineer (Applied AI, Patient)

Join Rula, a company at the forefront of AI/ML-powered patient experiences. As a Senior Staff Engineer, you will play a crucial role in building and scaling AI/ML capabilities across various areas, including matching, ranking, recommendations, onboarding, and personalization. This hands-on technical leadership position requires deep expertise in the ML lifecycle and experience in operating ML systems in regulated environments. Enjoy the flexibility of remote work, generous benefits, and the opportunity to make a significant impact in the healthcare industry.

• Develop and improve production models and AI-powered systems, building critical capabilities needed by Patient Engineering.
• Partner closely with Rula's ML team on shared infrastructure, models, standards, and architectural decisions to ensure alignment and collaboration.
• Lead the technical direction across teams, influencing architecture, mentoring senior engineers, and exercising strong judgment about when to use custom ML, foundation models/AI services, deterministic approaches, or hybrids.

• Strong evaluation and experimentation rigor connecting offline model performance to online experiments and product/business outcomes
• Deep hands-on ML expertise and technical leadership across the ML lifecycle: demonstrated experience with problem formulation, data/feature engineering, model development and training, evaluation, experimentation, deployment, monitoring, and continuous improvement, while setting technical direction across teams, influencing architecture, mentoring senior engineers, and exercising strong judgment about when to use custom ML, foundation models/AI services, deterministic approaches, or hybrids
• Hands-on experience building and operating modern foundation model/GenAI systems in production, including evaluation, reliability, latency, cost, privacy/safety, and operational tradeoffs
• Demonstrated 0→1 experience building shared ML infrastructure or reusable capabilities adopted across teams, such as feature infrastructure, training/inference pipelines, model serving, evaluation, observability, or lifecycle tooling
• 10+ years of software and/or ML engineering experience, including significant experience designing, building, deploying, and operating production ML systems with measurable product or business impact. Particularly Python, with experience building scalable systems and personally writing and reviewing production-quality code
• Deep experience with recommendation, ranking, relevance, personalization, search, or matching systems, including rigorous evaluation connecting offline model performance to online experiments and product/business outcomes
• Experience helping an organization evolve from early-stage or ad hoc ML development toward mature ML engineering practices
• Experience operating ML systems in healthcare, financial services, or another regulated/high-trust environment