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Lead Applied AI Engineer
Отличная позиция для лидера в области ИИ: прямой доступ к топ-менеджменту, работа над ключевыми бизнес-метриками и использование передового стека технологий. Компания является лидером рынка, что гарантирует масштаб и интересные задачи.
Сложность вакансии
Роль требует не только глубоких технических знаний в области LLM и RAG, но и способности напрямую взаимодействовать с руководством (COO) для определения стратегии ИИ. Высокий уровень ответственности за конечный бизнес-результат и необходимость работы в гибридном режиме повышают планку требований.
Анализ зарплаты
Указанная роль Lead уровня в сфере Applied AI в США (особенно в технологических компаниях) обычно оплачивается выше среднего по рынку труда Флориды из-за дефицита специалистов такого профиля. Рыночный диапазон для подобных позиций в США составляет $180k–$240k в год.
Сопроводительное письмо
I am writing to express my strong interest in the Lead Applied AI Engineer position at Material Bank. With over five years of experience in engineering and a proven track record of deploying production-grade AI systems, I am excited about the opportunity to bridge the gap between advanced LLM architectures and tangible business growth. My background in building RAG systems and agent-based frameworks aligns perfectly with your mission to automate internal workflows and enhance revenue-driving systems like sales prospecting and data enrichment.
Throughout my career, I have excelled at translating complex technical trade-offs into clear ROI for executive leadership, a skill I look forward to applying while partnering with your COO. I am particularly drawn to Material Bank's hybrid model in Boca Raton, as it fosters the high-bandwidth collaboration necessary for architecting scalable AI solutions. I am eager to bring my expertise in Python, TypeScript, and modern data infrastructure to help Material Bank scale its operations through innovative, applied AI.
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Описание вакансии
Material Bank is the world’s largest material marketplace for the architecture and design industry. Operating in 37 countries, our platform has become the standard for design professionals around the globe. Every day, Material Bank connects thousands of designers with tens of thousands of materials from leading brands. Material Bank is the fastest and most powerful way for design professionals to search, sample, and specify materials.
As an Applied AI Engineer, you will lead the design, build, and deployment of AI workflows across Material Bank. You will be responsible for creating innovative, scalable, and high-performing AI systems that improve internal efficiency while strengthening the core mechanics of how we operate. Your work will span two major domains (1) internal AI enablement - automating and optimizing internal workflows (e.g. project tracking, reporting, etc.) and (2) business-embedded AI - enhancing the systems and workflows that drive revenue and growth (e.g. sales prospecting, data enrichment, user support tools, etc.)
This role requires deep cross-functional collaboration. You will partner with technical teams (Engineering, Data) and non-technical stakeholders (Sales, Operations, Marketing, and Executive Leadership) to identify high-impact opportunities and translate business needs into practical, production-ready AI solutions. You must be equally comfortable architecting systems with engineers and communicating AI capabilities, trade-offs, and ROI to senior leaders.
You will prioritize initiatives based on true organizational value, balancing speed, feasibility, scalability, and long-term impact, and educate the executive team on where AI can create meaningful advantage and where focus matters most. This role requires a deep passion for applied AI, along with demonstrated experience deploying AI solutions in production and embedding them into operational and revenue-driving workflows. AI will be foundational to how Material Bank scales, and you will help architect that future.
This hybrid role is based in our Boca Raton headquarters, with three days per week onsite, partnering closely with our COO to drive AI initiatives.
What you’ll do:
- Design, build, and deploy end-to-end AI workflows from concept through production across internal and revenue-driving functions.
- Identify and prioritize high-impact AI opportunities that improve efficiency, decision-making, and organizational velocity.
- Embed AI into core business processes, including sales prospecting, brand vetting, material classification, data enrichment, and user support tools.
- Architect scalable solutions leveraging LLMs, embeddings, agent frameworks, automation tools, and modern data infrastructure.
- Partner cross-functionally with Engineering, Data, Sales, Marketing, Operations, and Executive Leadership to translate business needs into production-ready AI systems.
- Educate and advise leadership on AI capabilities, trade-offs, prioritization, and ROI to ensure focus on initiatives that drive the greatest business value.
- Continuously evaluate emerging AI technologies and ensure all implementations are built on clean, secure, and scalable data foundations.
What you’ll bring:
- 5+ years of engineering experience, with demonstrated success building and deploying production-grade AI/ML systems.
- Deep hands-on experience with LLMs, embeddings, RAG architectures, and/or agent-based frameworks.
- Strong programming proficiency in Python and/or TypeScript/JavaScript (additional languages a plus).
- Experience designing scalable data pipelines and working with SQL/NoSQL databases and modern data infrastructure.
- Proven ability to translate ambiguous business problems into practical, high-impact technical solutions.
- Experience collaborating across technical and non-technical teams, with strong communication skills at both operator and executive levels.
- Strong product and prioritization instincts, with a focus on measurable business impact and ROI.
- Passion for applied AI and a track record of integrating AI into operational and revenue-driving workflows.
*What you’ll get from us:*
- *Our people*: We are a growth-driven team that values efficiency, builds smart automation, operates in small empowered teams, and moves quickly from idea to execution.
- *Relaxation and Celebrations*: Flexible PTO, Sick Days, Paid National Holidays, and even more (ask us about this when we connect).
- *Health Benefits**: Wecontributeto your medical, dental, vision and short-term/long-termdisability plansand have a strong employee assistance program.*
- *Plan for your Retirement**: 401(k)eligibleafter your first 90 day's employed!*
- *Giving Back*: We sponsor multiple events throughout the year to help out our communities.
- *Growth*: We’ll help you take your career to the next level. We want you to be creative and take initiative which will allow you to grow and create within the company. Most importantly, be the best at what matters!
- *Flexible Work Schedules: With business units and employees across the globe, Material Technologies has embraced a hybrid working* model allowing department leaders to decide on the best approach for their respective teams, whether that be remote, in person, or a little of both.
Material Bank is proud to be an equal opportunity employer. We value diversity, and all applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, age, national origin, veteran or disability status or other status protected under any applicable federal, state or local law.
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Навыки
- Python
- TypeScript
- JavaScript
- LLM
- RAG
- SQL
- NoSQL
- Machine Learning
- Data Pipelines
- Embeddings
Возможные вопросы на собеседовании
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Как вы оцениваете ROI для потенциального ИИ-проекта перед началом разработки? Приведите пример, когда вы отказались от идеи из-за низкой эффективности.
Проверка технических знаний в области современных архитектур.
Какие стратегии оптимизации RAG (Retrieval-Augmented Generation) вы считаете наиболее эффективными для работы с большими объемами неструктурированных данных о товарах?
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Проверка опыта работы с данными.
Опишите ваш опыт проектирования пайплайнов данных для обучения или дообучения моделей: какие инструменты и базы данных (SQL/NoSQL) вы предпочитаете для задач эмбеддинга?
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