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Staff Machine Learning Engineer, Applications

Оценка ИИ

Отличная позиция в быстрорастущем Health-Tech стартапе с фокусом на передовые технологии (GenAI). Высокий балл за миссию компании, удаленный формат работы и возможность влиять на продукт на уровне Staff.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Роль уровня Staff требует не только глубоких технических знаний в области LLM и RAG, но и способности лидировать кросс-функциональные инициативы. Высокая сложность обусловлена необходимостью внедрения сложных AI-агентов в регулируемой сфере здравоохранения.

Анализ зарплаты

Медиана210 000 $
Рынок180 000 $ – 250 000 $
Оценка ИИ

Указанная роль Staff уровня в американском Health-Tech стартапе обычно предполагает компенсацию выше среднего по рынку. Наши оценки базируются на данных для опытных ML-инженеров в США, работающих удаленно.

Сопроводительное письмо

I am writing to express my strong interest in the Staff Machine Learning Engineer position at SmithRx. With over five years of experience in developing and deploying production-grade machine learning models, specifically focusing on Generative AI and Large Language Models, I am confident in my ability to drive innovation within your Application Engineering and AI team. My background includes extensive work with LangChain, PyTorch, and advanced retrieval methods (RAG), which aligns perfectly with your mission to build a next-generation drug acquisition platform.

Throughout my career, I have successfully led AI initiatives from ideation to deployment, balancing technical depth with the ability to communicate complex concepts to cross-functional stakeholders. I am particularly drawn to SmithRx's mission-driven culture and the opportunity to apply LLM-powered agents to improve productivity and member experiences in the healthcare sector. I look forward to the possibility of contributing my expertise in fine-tuning models and designing scalable AI architectures to your talented team.

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Откликнитесь в smithrx уже сейчас

Присоединяйтесь к SmithRx и станьте лидером в разработке AI-решений, которые трансформируют систему здравоохранения США!

Описание вакансии

Who We Are:

SmithRx is a rapidly growing, venture-backed Health-Tech company.  Our mission is to disrupt the expensive and inefficient Pharmacy Benefit Management (PBM) sector by building a next-generation drug acquisition platform driven by cutting edge technology, innovative cost saving tools, and best-in-class customer service.  With hundreds of thousands of members onboarded since 2016, SmithRx has a solution that is resonating with clients all across the country.

We pride ourselves for our mission-driven and collaborative culture that inspires our employees to do their best work. We believe that the U.S healthcare system is in need of transformation, and we come to work each day dedicated to making that change a reality. At our core, we are guided by our company values:

  • Integrity: Our purpose guides our actions and gives us confidence in the path ahead. With unwavering honesty and dependability, we embrace the pressure of challenging the old and exemplify ethical leadership to create the new.
  • Courage: We face continuous challenges with grit and resilience. We embrace the discomfort of the unknown by balancing autonomy with empathy, and ownership with vulnerability. We boldly challenge the status quo to keep moving forward—always.
  • Together: The success of SmithRx reflects the strength of our partnerships and the commitment of our team. Our shared values bind us together and make us one. When one falls, we all fall; when one rises, we all rise.

Job Summary:

SmithRx is leading the transformation of pharmacy benefit management (PBM) with a cutting-edge platform that delivers real-time insights, cost efficiencies, and exceptional customer experiences. We are seeking an experienced Data Scientist with expertise in generative AI as we continue to expand. In this key role, you will drive AI innovation and lead the implementation of AI-powered solutions. The position requires at least 5 years of experience in machine learning and AI development, with proven success leading AI initiatives. You will work with cross-functional leaders to deliver impactful AI solutions that directly influence our business outcomes.

What you will do:

As a member of the SmithRx Application Engineering and AI team, you will work customer facing as well as internal tooling that leverages AI to improve productivity and quality. You will work directly with software engineers, data engineers, product managers, and subject matter experts in the definition of new generative AI capabilities able to provide our members and internal organizations with the information they need to make proper decisions.

Qualified candidates will have an in-depth knowledge of the most common Large Language Models (LLMs) and Retrieval Methods, know how to train and fine-tune LLMs, and design and implement LLM-powered agents and tools at scale.

Responsibilities

  • Design Generative AI solutions, such as information retrieval and summary generation for support and operations organizations.
  • Partner with Software Engineering teams to build and deploy GenAI applications.
  • Ideate and explore opportunities to deploy GenAI technologies for customer-facing experiences
  • Drive the adoption and scalability of Generative AI capabilities within SmithRx; advocacy on the technology and evangelize the use of GenAI.

What you will bring to SmithRx:

  • 5+ years of experience in data science, machine learning, and AI development, with proven success leading AI initiatives
  • MS or PhD in Computer Science, Electrical Engineering, Statistics, Robotics or equivalent fields.
  • Applied Machine Learning experience (regression and classification, supervised, and unsupervised learning).
  • Strong mathematical background (linear algebra, calculus, probability, and statistics).
  • Proficiency in Python and object-oriented programming.
  • Strong experience working with machine learning and natural language processing techniques and tools.
  • Strong experience using Generative AI models, with a good understanding of deep learning model classes such as GPT, VAE, and GANs, as well as their hyperparameters.
  • Strong experience with retrieval methods e.g. using embeddings.
  • Strong experience using key Python packages for data wrangling, machine learning and deep learning such as pandas, sklearn, TensorFlow, torch, transformers, LangChain, etc.
  • Experience in Prompt Engineering and few-shot techniques to enhance LLM's performance on specific tasks.
  • Experience with training and fine-tuning deep learning models, especially LLMs, and how to tune hyperparameters to ensure task generalization.
  • Ability to drive a project and work both independently and within a cross-functional team.
  • Excellent verbal and written communication, able to articulate complex concepts with a non-technical audience.

Preferred Qualifications

  • Experience with embedding model training and retrieval method evaluation approaches.
  • Experience with LLM architectures, adapters, Mixture of Experts (MoEs) pretraining and fine-tuning techniques.
  • Experience with design, deployment, and evaluation of LLM-powered agents and tools and orchestration approaches.
  • Experience with reinforcement learning approaches in the context of fine-tuning LLM outputs.
  • Experience with time series analysis and multivariate time series modeling.

What SmithRx Offers You:

  • Highly competitive wellness benefits including Medical, Pharmacy, Dental, Vision, and Life Insurance and AD&D Insurance
  • Flexible Spending Benefits
  • 401(k) Retirement Savings Program
  • Short-term and long-term disability
  • Discretionary Paid Time Off
  • Paid Company Holidays
  • Wellness Benefits
  • Commuter Benefits
  • Paid Parental Leave benefits
  • Employee Assistance Program (EAP)
  • Well-stocked kitchen in office locations
  • Professional development and training opportunities
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Навыки

  • Python
  • Pandas
  • PyTorch
  • Machine Learning
  • LLM
  • Statistics
  • Deep Learning
  • Transformers
  • RAG
  • Prompt Engineering
  • Scikit-learn
  • TensorFlow
  • Generative AI
  • LangChain
  • Natural Language Processing

Возможные вопросы на собеседовании

Проверка опыта проектирования сложных систем на базе LLM.

Расскажите о наиболее сложной архитектуре LLM-агента, которую вы разработали: с какими проблемами масштабируемости вы столкнулись?

Оценка навыков оптимизации поиска информации, что критично для RAG-систем.

Какие стратегии вы используете для оценки и улучшения качества поиска (retrieval) в системах с использованием эмбеддингов?

Проверка практического опыта дообучения моделей.

В каких случаях вы бы предпочли дообучение (fine-tuning) модели вместо использования Prompt Engineering или RAG? Приведите примеры из практики.

Оценка способности работать в междисциплинарной команде.

Как вы подходите к объяснению ограничений и рисков генеративного AI (например, галлюцинаций) нетехническим стейкхолдерам и менеджерам по продукту?

Проверка знаний современных архитектур.

Каков ваш опыт работы с техниками Mixture of Experts (MoE) или адаптерами (LoRA/QLoRA) при оптимизации LLM?

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