Sr. Staff Engineer - Applied AI, Patient (Remote)
Rula · Remote - United States
About this role
## About Rula Rula is a remote-first company focused on making mental healthcare work for everyone. We believe mental health is as important as physical health, and we’re dedicated to treating the whole person with evidence-based and compassionate care. **Location:** Remote-first (hiring in most U.S. states; **not hiring in Hawaii**). --- ## About the Role: Sr. Staff Engineer - Applied AI, Patient We’re hiring a **Sr. Staff Engineer – Applied AI, Patient** to help **Patient Engineering** build and scale **AI/ML-powered patient experiences** across: - Matching - Ranking - Recommendations - Onboarding - Personalization This is a **deeply hands-on technical leadership** role. You’ll develop and improve **production models and AI-powered systems**, build critical capabilities for Patient Engineering, and partner closely with Rula’s ML team on shared infrastructure, models, standards, and architectural decisions. You won’t own a single model or product surface—you’ll go where the **highest-leverage Applied AI problems** are, including: - Developing models - Building reusable capabilities - Improving existing systems - Determining whether to use **custom ML**, **foundation model/AI services**, **deterministic software**, or a **hybrid approach** **Success looks like:** enabling Patient Engineering to build better intelligent experiences through stronger technical partnership, reusable capabilities, and faster, higher-quality delivery of AI/ML-powered patient experiences. --- ## Required Qualifications - **10+ years** of software and/or ML engineering experience, including significant experience designing, building, deploying, and operating **production ML systems** with measurable product/business impact. - **Strong Python** experience; ability to write and review production-quality code. - Deep hands-on ML expertise and technical leadership across the ML lifecycle: - Problem formulation - Data/feature engineering - Model development & training - Evaluation - Experimentation - Deployment - Monitoring - Continuous improvement - Setting technical direction, influencing architecture, mentoring senior engineers, and strong judgment on approach (custom ML vs foundation models vs deterministic vs hybrid). - Demonstrated **0→1** experience building shared ML infrastructure or reusable capabilities adopted across teams (e.g., feature infrastructure, training/inference pipelines, model serving, evaluation, observability, lifecycle tooling). - Deep experience with **recommendation, ranking, relevance, personalization, search, or matching** systems, including rigorous evaluation connecting offline performance to online experiments and outcomes. - Strong evaluation and experimentation rigor connecting offline model performance to online experiments and product/business outcomes. - Hands-on experience building and operating modern **foundation model/GenAI** systems in production, including tradeoffs across: - Reliability - Latency - Cost - Privacy/safety - Operational considerations. --- ## Preferred Qualifications - Experience helping an organization evolve from early-stage/ad hoc ML to mature ML engineering practices. - Experience operating ML systems in **healthcare**, **financial services**, or another regulated/high-trust environment. --- ## Benefits (Full-Time) - **100% remote** work environment (U.S.-based; not hiring in Hawaii) - Attractive pay and benefits with **full transparency of pay ranges** - Comprehe
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