- Страна
- США
- Зарплата
- 197 500 $ – 241 000 $
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Senior Machine Learning Engineer, Applied AI
Отличная вакансия с высокой прозрачной зарплатой, современным стеком (LLM, RAG) и социально значимым продуктом. Компания предлагает расширенный пакет бенефитов и культуру экспериментов.
Сложность вакансии
Роль требует глубоких знаний в области LLM, RAG и MLOps, а также умения работать в регулируемой сфере здравоохранения. Высокий уровень ответственности за полный цикл разработки — от прототипа до продакшена.
Анализ зарплаты
Предлагаемый диапазон $197k - $241k полностью соответствует рыночным стандартам для Senior ML ролей в США, особенно в секторе HealthTech. Верхняя граница даже несколько превышает медиану, что делает предложение очень конкурентоспособным.
Сопроводительное письмо
I am writing to express my strong interest in the Senior Machine Learning Engineer, Applied AI position at SimplePractice. With over 5 years of experience in developing and deploying machine learning models, I am particularly drawn to your mission of reducing administrative burden for clinicians through innovative AI workflows. My background in building robust LLM-driven systems and my proficiency in Python and AWS align perfectly with the requirements of this role.
In my previous projects, I have successfully navigated the entire ML lifecycle, from initial prototyping and prompt engineering to production-level deployment and monitoring. I am especially excited about the opportunity to work on RAG architectures and retrieval pipelines within a healthcare context, where quality and safety are paramount. I am confident that my problem-oriented mindset and experience with ML orchestration platforms like Outerbounds will allow me to contribute effectively to your team's roadmap and the evolution of your ML platform.
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Описание вакансии
About Us
At SimplePractice, we are improving access to quality care by equipping health and wellness clinicians with all the tools they need to thrive in private practice.
More than 250,000 providers trust SimplePractice to build their business through our industry-leading software with powerful tools that simplify every part of practice management. From admin work to clinical care, our suite of innovative solutions work together to reduce administrative burden—empowering solo and small group practitioners to thrive alongside their clients.
Recognized by MedTech Breakthrough as the Best Practice Management Solution Provider in 2024 and the Digital Health Awards in 2023, SimplePractice is proud to pave the future of health tech.
The Role
Our team is dedicated to empowering clinicians through data-driven innovations. We combine rigorous data science with practical engineering to build systems that make daily workflows more efficient, insightful, and intuitive. If you love tackling challenging problems and turning data into meaningful outcomes, you’ll find a welcoming and dynamic environment here.
As a Senior Machine Learning Engineer, Applied AI, you’ll be at the forefront of building product features that help clinicians work effectively and efficiently, providing quality care to patients. You’ll be designing experiments, building robust models, tuning prompts, implementing LLM evals, and driving projects from idea to prototype to production with product, engineering and devOps teams. You’ll also play an important role in the roadmapping exercises of our ML platform.
We value mentorship, open communication, and pushing the boundaries of what AI can do in a real-world healthcare context. Whether you’re fine-tuning a model, presenting insights to stakeholders, or brainstorming new product features, your work will have a direct and meaningful impact.
Responsibilities
- AI Prototyping and Development
- Develop AI workflows, customize data pipelines, tune models, and engineer prompts to bring idea to prototype
- Work with subject matter experts to set up evaluation for AI workflows, ensuring rigor, quality and safety of content output
- Work with eng partners to integrate AI workflows into production
- Build and configure AI performance monitoring with proper reporting and alerts
- Optimize and maintain AI workflows for performance, reliability, and long-term scalability
- Research
- Start with the Job-to-be-done, dive deep into the domain and understand the problem from user perspective
- Decompose problems into conquerable pieces. Design solutions to address each with cross-disciplinary thinking and big picture in mind.
- Conduct exploratory data analysis to answer key questions and test assumptions. Design experiments and build prototypes for proof-of-concept.
- Build artifacts to illustrate the findings with rigor and how they inform the roadmap and decisions
- Cross-Functional Collaboration
- Provide AI expert advice to product and eng partners in shaping product roadmap
- Partner closely with software eng, product, data eng, ML platform teams to scope and plan in execution
- Communicate timeline, milestones, findings w/ internal stakeholders
- Mentor & Advocate Best Practices
- Guide less experienced team members, sharing knowledge on LLM workflows and AI/ML model lifecycle
- Champion a culture of experimentation, continuous learning, and proactive problem-solving
- Drive Innovation
- Stay current with emerging ML tools and technologies, integrating new techniques that elevate our product capabilities
- Look for creative ways to leverage data to make clinicians’ lives easier, more efficient, and more effective
Desired Skills & Experience
- BS or above in Computer Science, Statistics or a related technical field
- 5+ years of experience in Machine Learning, with proven track record of bringing ideas to life, from prototype to productized features
- Strong proficiency in Python and hands-on with advanced data analysis tools
- Strong skills in data engineering and self-sufficient in data pipelines for the AI workflow
- Experience with AWS (or other cloud platforms) for model deployment
- Comfortable designing and evaluating LLM-driven workflows
- Familiarity with retrieval pipelines and vector databases
- Problem-oriented mindset with strong cross-disciplinary thinking and a bias toward simplicity and clarity in solving problems
- Comfort working with remote teams, using GitHub, Slack, Notion, and Zoom
- Proficiency in English with strong communication and collaboration skill
Bonus Points
- Experience with RAG architecture and context/state management for LLMs
- Familiarity with LLM eval tools and human-in-the-loop evaluation process
- Experience with Outerbounds or similar ML orchestration platforms
- Experience with Argo Flows for CI/CD
- Experience with prompt management tool like Langfuse
- Familiarity with Kubernetes for container orchestration
- Background in healthcare, clinical workflows, or regulated domains
Base Compensation Range
$197,500 - $241,000
Base salary is one component of total compensation. Employees may also be eligible for an annual bonus or commission. Some roles may also be eligible for overtime pay.
The above represents the expected base compensation range for this job requisition. Ultimately, in determining your pay, we’ll consider many factors including, but not limited to, skills, experience, qualifications, geographic location, and other job-related factors.
Benefits
We offer a competitive benefits program including:
- Medical, dental, vision, life & disability insurance
- 401(k) plan with company match
- Flexible Time Off (FTO), wellbeing days, paid holidays, and summer Fridays
- Mental health resources
- Paid parental leave & Backup Care
- Tuition reimbursement
- Employee Resource Groups (ERGs)
California Job Applicant Privacy Notice
Thank you for your interest in opportunities at SimplePractice LLC (“SimplePractice” or “us” or “we” or “our”). Please note that when you submit your resume or application materials to us for employment purposes, you are subject to the SimplePractice California Job Applicant Privacy Notice.
For more information about our privacy practices, please contact us at privacy@simplepractice.com.
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Навыки
- Python
- Machine Learning
- AWS
- LLM
- RAG
- Vector Databases
- Kubernetes
- GitHub
- Data Engineering
- MLOps
Возможные вопросы на собеседовании
Проверка практического опыта работы с современными ИИ-архитектурами.
Расскажите о вашем опыте проектирования и оптимизации RAG-систем. С какими основными проблемами вы сталкивались?
Важно для обеспечения качества в медицинском продукте.
Как вы подходите к оценке (evaluation) ответов LLM, особенно в вопросах безопасности и точности медицинского контента?
Оценка навыков интеграции моделей в реальную инфраструктуру.
Опишите ваш опыт работы с ML-оркестрацией (например, Outerbounds или Argo Flows) для деплоя моделей в AWS.
Проверка умения работать с данными самостоятельно.
Какие инструменты и подходы вы используете для построения и поддержки собственных пайплайнов данных для обучения моделей?
Оценка лидерских качеств и умения работать в команде.
Как вы подходите к менторству младших коллег и внедрению лучших практик разработки (best practices) в ML-команде?
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- Страна
- США
- Зарплата
- 197 500 $ – 241 000 $