Senior Staff Engineer (Applied AI, Patient)

Rula; United States; Remote; USD 180,000 - 240,000; Full time

Benefits offered by this job

Remote work in US

401(k) plan

Health, dental, vision insurance

Parental leave

Employee assistance program

Home office equipment

Pay/benefits not geo-locked

Company shutdown weeks

Job summary

Rula seeks a Sr. Staff Engineer – Applied AI, Patient to lead hands-on ML work across patient experiences, including matching, ranking, recommendations and onboarding.

You will advance production models, build reusable capabilities, and collaborate closely with the ML team on infrastructure, standards, and architecture decisions. You will drive 0→1 and 1→n advancements, evaluate model tradeoffs (custom ML vs foundation models), and mentor senior engineers while shaping a scalable, compliant AI

Qualifications

• Deep hands-on ML expertise across the ML lifecycle, from data and feature engineering to deployment and monitoring.
• Proven ability to design and scale production ML systems with measurable impact.
• Strong Python proficiency and experience with large-scale model evaluation.

Responsibilities

• Lead hands-on ML efforts for patient experiences, including matching, ranking, and personalization.
• Build reusable ML infrastructure and partner with ML team on standards and architecture decisions.
• Evaluate tradeoffs between custom ML, foundation models, and deterministic approaches.
• Mentor engineers and drive high-quality delivery of AI/ML-powered patient experiences.

Skills

ML leadership

Production ML

Model evaluation

Python

System design

Foundation models

Job description

• We’re hiring a Sr. Staff Engineer – Applied AI, Patient to help Patient Engineering build and scale AI/ML-powered patient experiences across areas such as matching, ranking, recommendations, onboarding, and personalization
• Rula is at an important stage in its ML journey. We already have ML models and pipelines powering product experiences, with additional capabilities under development. At the same time, much of the engineering foundation required to develop, deploy, evaluate, and operate ML consistently at scale is still ahead of us
• This is a deeply hands‑on technical leadership role. You’ll develop and improve production models and AI‑powered systems, build critical capabilities needed by 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—developing models, building reusable capabilities, improving existing systems, or determining whether a problem is best solved with custom ML, a foundation model/AI service, deterministic software, or a hybrid approach
• Success means enabling Patient Engineering to build better intelligent experiences while creating a strong technical partnership with Rula’s ML team—resulting in better models, reusable capabilities, and faster, higher‑quality delivery of AI/ML-powered patient experiences

Benefits

• The ability to work remotely from any location in the US
• Two company‑wide shut‑down weeks a year for self‑care
• Access to 401(k) personal retirement plan
• Medical, dental, and vision employer insurance plans
• Paid parental leave to support you and your family
• Employee Assistance Programs (EAP) through our insurance
• The equipment you need for your home office
• Pay and benefits that aren’t geo‑locked

Strong evaluation and experimentation rigor connecting offline model performance to online experiments and product/business outcomesDeep 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 hybridsHands‑on experience building and operating modern foundation model/GenAI systems in production, including evaluation, reliability, latency, cost, privacy/safety, and operational tradeoffsDemonstrated 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 tooling10+ 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 codeDeep experience with recommendation, ranking, relevance, personalization, search, or matching systems, including rigorous evaluation connecting offline model performance to online experiments and product/business outcomesExperience helping an organization evolve from early-stage or ad hoc ML development toward mature ML engineering practicesExperience operating ML systems in healthcare, financial services, or another regulated/high‑trust environment