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Rula

Sr. Staff Engineer - Applied AI, Patient (Remote)

Rula is hiring a Sr. Staff Engineer - Applied AI, Patient (Remote) for a remote role open to applicants in United States. It pays $242k-$288k a year and Level rates it ; you can apply on Rula.

Level's read on this job

  • On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
  • The posting lists this role as remote.
  • Tools and skills the posting cites include Python, ML Pipelines and applied AI.
  • Level classifies it as a lead role.
  • Level lists 31 other open roles at Rula.

AI in this role

Lead applied AI initiatives for patient engineering at Rula, scaling ML-powered experiences such as matching and personalization.

PythonML Pipelines
applied-aimachine-learningsoftware-engineeringsystem-architecture

We believe that mental health is just as important as physical health. We recognize that mental health issues can be complex and multifaceted, and we are dedicated to treating the whole person, not just the symptoms.

We aim to create a world where mental health is no longer stigmatized or marginalized, but rather is embraced as an integral part of one's overall well-being.

We believe that by providing quality care that is both evidence-based and compassionate, we can empower individuals to take charge of their mental health and achieve their full potential. We are passionate about making a positive impact on the lives of those struggling with mental health issues and we strive to be a force for positive change in the field of mental healthcare.

Rula is a remote-first company. We currently hire in most U.S. states, with the exception of Hawaii.

About the Role

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.

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 or business impact. Particularly Python, with experience building scalable systems and personally writing and reviewing production-quality code.

  • 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.

  • 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.

  • 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.

  • 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 evaluation, reliability, latency, cost, privacy/safety, and operational tradeoffs.

Preferred Qualifications

  • 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.

We're serious about your well-being! As part of our team, full-time employees receive:

  • 100% remote work environment: Working hours to support a healthy work-life balance, ensuring you can meet both professional and personal commitments (must be based in United States, currently not hiring in Hawaii)

  • Attractive pay and benefits: Full transparency of pay ranges regardless of where you live in the United States

  • Comprehensive health benefits: Medical, dental, vision, life, disability, and FSA/HSA

  • 401(k) plan access: Start saving for your future

  • Generous time-off policies: Including 2 company-wide shutdown weeks each year for self-care (for most employees)

  • Paid parental leave: Available for all parents, including birthing, non-birthing, adopting, and fostering

  • Employee Assistance Program (EAP): Supporting your mental and physical health

  • Quarterly department stipend: Fun team-building activities or in-person gatherings

  • Community and employee resource groups: Participate in groups that celebrate employee identity and lived experiences, fostering a sense of community and belonging for all

  • Home office stipend: New hire home office stipend & $50 monthly stipend to help cover internet or cell phone expenses

  • Wellness at Rula program: Year-round wellness initiatives and a $50/month wellness stipend

Our team

We believe that diversity, equity, and inclusion are fundamental to our mission of making mental healthcare work for everyone. We are dedicated to having a culture of inclusion that will support our employees in feeling safe, seen, heard, and valued.

How we rate this

Sr. Staff Engineer - Applied AI, Patient (Remote) at Rula rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Applied AIMachine learningSoftware EngineeringSystem ArchitecturePythonML Pipelines

Questions you could be asked

  1. Tell me about a project where applied ai was part of your work. What did you do?
  2. Tell me about a project where machine learning was part of your work. What did you do?
  3. Tell me about a project where software engineering was part of your work. What did you do?
  4. Tell me about a project where system architecture was part of your work. What did you do?
  5. Walk me through how you've used Python in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: Applied AI, Machine learning, Software Engineering, System Architecture, and Python. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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