yandex
E
evolutioniq
Страна
США
Зарплата
185 000 $ – 235 000 $
+500% приглашений

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Ускорим процесс поиска работы
SeniorУдалённоПолная занятость

Senior Software Engineer - AI / LLM (Medhub)

Оценка ИИ

Отличная вакансия с высокой зарплатой, прозрачными бонусами и опционами. Компания признана одним из лучших работодателей, предлагает сильный соцпакет и работу над социально значимым продуктом.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Роль требует глубоких знаний в области LLM, RAG и статистического машинного обучения, а также умения переносить научные исследования в промышленный код. Высокая планка ответственности за архитектуру в быстрорастущем стартапе.

Анализ зарплаты

Медиана200 000 $
Рынок170 000 $ – 240 000 $
Оценка ИИ

Предлагаемый диапазон $185k–$235k полностью соответствует и даже немного превышает рыночные стандарты для Senior AI Engineer в Нью-Йорке и удаленно по США. Дополнительные бонусы и опционы делают предложение крайне конкурентоспособным.

Сопроводительное письмо

I am writing to express my strong interest in the Senior Software Engineer - AI / LLM position at EvolutionIQ. With extensive experience in developing performant Python applications and a proven track record of deploying LLM-powered products, I am excited about the opportunity to contribute to your industry-leading medical synthesis platform. My expertise in hybrid machine learning approaches, specifically combining RAG with embeddings-based models, aligns perfectly with your mission to deliver accurate and efficient claims handling.

In my previous roles, I have successfully translated cutting-edge AI research into production-grade solutions within microservice architectures. I am particularly drawn to EvolutionIQ's engineering culture of simplicity and collaboration, and I am eager to apply my skills in prompt engineering and model evaluation to enhance your claim synthesis product. I look forward to the possibility of discussing how my background in building scalable AI systems can help drive EvolutionIQ's continued growth.

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Откликнитесь в evolutioniq уже сейчас

Присоединяйтесь к лидерам AI-трансформации в страховании и создавайте инновационные решения на базе LLM и RAG!

Описание вакансии

About Us:EvolutionIQ’s mission is to deliver state of the art technology that helps insurance claims teams make claims handling more accurate, fair, and efficient, so that more people impacted by injury or illness can continue their lives with dignity and stability. We are currently experiencing massive growth and to accomplish our goals, we are hiring world-class talent who want to help build and scale internally, and transform the insurance space. Our team is our #1 priority, and we have been named one ofInc.’s Best Workplaces 3 years in a row and Built In’s Best Places to work in 2025 and 2026!

The Adventure:We're the leading AI Guidance Platform in the insurance industry today, working with some of the largest insurance carriers in the US and expanding globally. We are growing incredibly fast and have a nearly 100% success record in converting pilots to production deployments. You will be designing on a shared, unified platform that can scale across multiple lines of business. Our engineering culture values simplicity, core engineering principles, quality, honesty, transparency and strong collaboration. If you’re excited to work on a fast-moving enterprise engineering team using the latest technologies at high scale, we want to meet you.

About You: As an ambitious Senior AI/LLM Engineer, you will play a key role in advancing our industry-leading medical synthesis product. You take strong ownership of your work and have a proven track record of driving projects end-to-end, from ideation through deployment. Thriving in a fast-paced startup environment, you stay current with the latest AI and machine learning research. You’re passionate about applying hybrid approaches that combine large language models (LLMs), statistical machine learning techniques, and retrieval-augmented generation (RAG) with embeddings-based models to deliver better, more reliable outcomes for our users.

In this Role You Will:

  • Design, build, and deploy AI-powered and LLM-driven features for our claim synthesis product, including robust extraction of key information from complex medical documents and human-in-the-loop summarization workflows
  • Develop and implement hybrid machine learning solutions that leverage statistical models, LLMs, and embeddings-based retrieval techniques such as RAG to improve system accuracy, scalability, and robustness
  • Write clean, scalable, and efficient code while optimizing the performance of existing AI/ML systems in production
  • Collaborate closely with data labelers and subject matter experts (SMEs) to rigorously evaluate AI system outputs and continuously improve model performance
  • Partner with Product teams to rapidly iterate on feedback and deliver impactful features
  • Translate cutting-edge AI/ML research and novel techniques into production-grade, reliable, and maintainable solutions that operate seamlessly in live customer environments

Skills Requirements:

  • 3+ years of experience writing performant Python code following modern best practices
  • Minimum 1 year of experience building and deploying products powered by large language models (LLMs) in fast-paced, professional environments
  • Hands-on experience with statistical machine learning techniques as well as hybrid approaches combining LLMs, retrieval-augmented generation (RAG), and embeddings-based models, including vector search and similarity measures
  • Proven ability to build and integrate API services within service-oriented or microservice architectures
  • Strong skills in evaluating and interpreting LLM outputs and AI model predictions, with a focus on aligning model behavior with real-world business and user outcomes
  • Expertise in prompt engineering and fine-tuning of large language models for domain-specific applications

Bonus Points:

  • Experience translating state-of-the-art AI/ML research into production code
  • Comfortable collaborating with data labelers and subject matter experts to improve training data and evaluation processes
  • Experience building agentic or autonomous AI systems in production
  • Background working with multimodal data (e.g., images, audio)

Work-life, Culture & Perks:

  • Compensation: The base salary range is $185-235K, with flexibility depending on a candidate’s background and experience. An annual bonus plan and company equity plan (RSUs) are also included in our compensation package.
  • Well-Being: Medical, dental, vision, short & long-term disability, life insurance and AD&D, and 401k matching. Additional family, wellness, and pet benefits.
  • Home & Family: Paid time off and sick leave, 100% paid parental leave (16 weeks for primary caregivers and 12 weeks for secondary caregivers). We offer a flexible schedule for new parents returning to work.
  • Office Life: Catered lunches, happy hours, pet-friendly spaces, and monthly technology stipend.
  • Growth & Training: $1,000/year for each employee for professional development, as well opportunities for tuition reimbursement.
  • Sponsorship: We are open to sponsoring candidates currently in the U.S. who need to transfer their active visa. Please check with our Recruiting team if your visa is applicable for transfer.

EvolutionIQ appreciates your interest in our company as a place of employment. EvolutionIQ is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

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Навыки

  • Python
  • Machine Learning
  • LLM
  • Microservices
  • RAG
  • Prompt Engineering
  • API
  • Vector Search
  • Embeddings

Возможные вопросы на собеседовании

Проверка практического опыта работы с RAG и понимания архитектурных компромиссов.

Расскажите о наиболее сложной системе RAG, которую вы внедряли: как вы решали проблему релевантности поиска и галлюцинаций модели?

Оценка навыков работы с данными в специфической области (медицина/страхование).

Как бы вы организовали процесс оценки качества (evaluation) ответов LLM для медицинских документов, учитывая необходимость высокой точности?

Проверка инженерных навыков в контексте Python и производительности.

Какие методы оптимизации производительности Python-сервисов вы используете при обработке больших объемов неструктурированных данных?

Оценка понимания современных трендов и агентских систем.

В каких случаях, по вашему мнению, оправдано использование агентских (agentic) систем вместо линейных цепочек вызовов LLM?

Проверка умения работать в кросс-функциональной команде.

Опишите ваш опыт взаимодействия с экспертами предметной области (SMEs) для улучшения качества обучающих выборок или промптов.

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E
evolutioniq
Страна
США
Зарплата
185 000 $ – 235 000 $