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SeniorГибридПолная занятость

Senior AI Engineer - Detect

Оценка ИИ

Отличная вакансия с четкой социальной миссией, конкурентной зарплатой и современным стеком. Работа в Нью-Йорке в гибридном формате и участие в создании продукта с нуля делают это предложение крайне привлекательным для Senior-специалистов.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Роль требует редкого сочетания навыков Fullstack-разработки и глубокой экспертизы в прикладном ML (VLM, LLM). Высокая ответственность за точность систем, спасающих жизни, и необходимость развивать инфраструктуру с нуля повышают планку сложности.

Анализ зарплаты

Медиана195 000 $
Рынок170 000 $ – 230 000 $
Оценка ИИ

Предлагаемый диапазон $180k - $210k полностью соответствует рыночным ожиданиям для Senior AI Engineer в Нью-Йорке. Верхняя граница даже немного превышает медиану для стартапов данной стадии, учитывая дополнительные бонусы и опционы.

Сопроводительное письмо

I am writing to express my strong interest in the Senior AI Engineer - Detect position at Sage. With over five years of experience in software engineering and a deep focus on production-grade machine learning, I am particularly drawn to Sage's mission of improving care for older adults through innovative technology. My background in building end-to-end ML pipelines and working with multi-modal vision models aligns perfectly with your goal of enhancing real-time fall detection.

In my previous roles, I have successfully matured early-stage ML experimentation platforms and designed repeatable fine-tuning pipelines that significantly improved model accuracy. I am a full-stack capable engineer proficient in Python and TypeScript, and I have extensive experience with cloud AI platforms like Google Vertex AI. I am excited by the prospect of applying these skills to reduce false positives and expand Sage's detection capabilities into new behavioral categories.

What excites me most about Sage is the opportunity to work in a high-autonomy environment where engineering decisions directly impact the safety of seniors. I thrive in mission-driven teams that treat their work as a marathon, and I am eager to contribute to your vision of modernizing emergency response systems. Thank you for considering my application.

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Описание вакансии

Sage is on a mission to improve care and quality of life for older adults, starting with those residing in senior living facilities. Falls are the leading cause of injury-related death among adults over 65. And yet, fall prevention and emergency response systems for older adults are archaic and ineffective. At Sage we've built a more modern way of understanding when older adults need help, including methods for residents to alert caregivers when in need of help, and corresponding software for caregivers to triage response. Our company mission is to create a product that our client counterparts love, and this role is a key part of that objective.

Sage is a small, tight team of ambitious, multi-disciplinary entrepreneurs. We are a software-enabled, mission-driven company, and are focused only on the problems that are central to achieving that mission. At Sage, we work hard and fast but also know that to build a truly important company, we need to treat our work as a marathon, and not a sprint. The journey matters.

About this Role

As a Senior Engineer on the Detect team working on applied machine learning, you're a fullstack engineer who owns the end-to-end lifecycle of the AI models that power our camera-based detection system — from data collection and labeling through prompting, training, evaluation, and production deployment. Today, Detect uses frontier multi-modal vision models to analyze video streams and detect falls in real time. Your job is to make these external models dramatically better and more capable.

You'll take ownership of our ML experimentation platform and infrastructure, maturing it into a robust system that enables the team to rapidly iterate on model quality at scale. You'll design repeatable fine-tuning pipelines that allow models to continuously improve with new production data, and expand the system's detection capabilities beyond falls into new behavioral categories. This is a hands-on, high-autonomy role where you'll directly impact the accuracy of a life-saving system used every day by caregivers across the country.

Responsibilities

  • Improve detection accuracy and reduce false positives through prompt engineering, model fine-tuning, and novel inference strategies
  • Work closely with the backend engineering team to integrate model improvements into the real-time video processing pipeline
  • Own and evolve our ML experimentation platform, maturing existing infrastructure into a production-grade system the team relies on daily
  • Build data pipelines for collecting, labeling, and preparing production video and image data for model training
  • Design repeatable fine-tuning and evaluation pipelines that enable rapid experimentation and measure model performance at scale
  • Expand detection capabilities into new behavioral categories

Minimum Qualifications

  • 5+ years of professional software engineering experience
  • Experience training, fine-tuning, or improving external ML/AI models in a production setting
  • Strong understanding of model evaluation methodology and experiment tracking
  • Proficiency in Python or TypeScript

Preferred Qualifications

  • Experience with cloud AI platforms (Google Vertex AI, AWS SageMaker, or similar)
  • Experience fine-tuning multi-modal models (VLMs) or large language models (LLMs)
  • Familiarity with Kotlin, Java, or similar JVM languages
  • Full-stack capability with TypeScript/React for building internal tool UIs
  • Background in computer vision, video processing, or working with image/video data at scale
  • Experience building internal ML tooling (labeling, experiment tracking, evaluation)
  • Experience maturing early-stage internal tools into production-grade systems

Benefits and Pay

Our headquarters are located in New York City's Union Square. We believe in cross team collaboration. We think good ideas can come from anyone, and we've designed our processes to encourage participation from all. While we take our mission seriously, we don't take ourselves too seriously. We like to host offsites, outings, and team meals where we can connect as people, not just as colleagues. We offer office lunch and a fully stocked snack bar. While we are an in office culture, we allow up to 2 remote days per week.

Our benefits package for employees includes competitive base compensation along with stock options. The expected annual salary range for this role is $180,000 - $210,000 USD, depending upon the job level, which will depend on your level of expertise, your experience, and your qualifications. We also provide fully-paid health and dental insurance coverage for all of our employees, along with other health benefits including vision insurance, membership to premium primary and urgent care, and online medical health providers. We also have a take as you need time off policy, in addition to 7 paid holidays and a company wide winter break during the holidays.

EEO Statement

Sage is an equal opportunity employer that is committed to diversity and inclusion in the workplace. We prohibit discrimination and harassment of any kind based on race, color, sex, religion, sexual orientation, national origin, disability, genetic information, pregnancy, or any other protected characteristic as outlined by federal, state, or local laws.

This policy applies to all employment practices within our organization, including hiring, recruiting, promotion, termination, layoff, recall, leave of absence, compensation, benefits, training, and apprenticeship. Sage makes hiring decisions based solely on qualifications, merit, and business needs at the time.

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Навыки

  • TypeScript
  • Python
  • Machine Learning
  • Large Language Models
  • Computer Vision
  • React
  • Backend Development
  • Kotlin
  • Java
  • Data Pipelines
  • Google Vertex AI
  • AWS SageMaker

Возможные вопросы на собеседовании

Проверка опыта работы с видеоданными и понимания специфики real-time систем.

Как бы вы оптимизировали инференс мультимодальной модели для обработки видеопотока в реальном времени, чтобы минимизировать задержку при обнаружении падений?

Оценка навыков работы с данными и борьбы с ложноположительными срабатываниями.

Какие стратегии сбора и разметки данных вы бы использовали для уменьшения количества ложных срабатываний в условиях ограниченного датасета специфических движений пожилых людей?

Проверка инженерного подхода к построению ML-инфраструктуры.

Опишите ваш подход к созданию воспроизводимого пайплайна дообучения (fine-tuning) моделей. Какие инструменты для трекинга экспериментов вы считаете наиболее эффективными для стартапа?

Оценка Fullstack-навыков и умения интегрировать ML в продукт.

Расскажите о вашем опыте интеграции ML-моделей в бэкенд-архитектуру. Как вы обеспечиваете мониторинг качества модели после деплоя в продакшн?

Проверка способности расширять функционал системы.

Как бы вы подошли к задаче классификации новых типов поведения (например, прием лекарств или нарушение сна), используя уже имеющуюся инфраструктуру для детекции падений?

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sage49
Страна
США
Зарплата
180 000 $ – 210 000 $