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Machine Learning Engineer – Feed Recommendation
Оценка ИИAppLovin — признанный лидер в сфере рекламных технологий и AI, предлагающий работу над амбициозным проектом с нуля. Вакансия привлекательна высокой степенью ответственности, сильной инженерной культурой и конкурентной средой в Сингапуре.
Сложность вакансии
Оценка ИИРоль требует глубоких знаний в области рекомендательных систем (Recall, Ranking, CTR) и опыта работы с высоконагруженными ML-системами. Ожидается владение PyTorch/TensorFlow и понимание продуктовых метрик, таких как удержание (retention) и монетизация.
Анализ зарплаты
Оценка ИИЗарплата в вакансии не указана, но для позиции ML-инженера с опытом 3-5 лет в Сингапуре рыночный диапазон составляет от 8,000 до 13,000 SGD в месяц. AppLovin как крупная технологическая компания обычно предлагает компенсацию на уровне верхнего дециля рынка, включая бонусы и опционы.
Сопроводительное письмо
I am writing to express my strong interest in the Machine Learning Engineer position for Feed Recommendation at AppLovin. With over 4 years of experience in developing production-grade ML systems, I have a proven track record in building scalable recommendation engines, specifically focusing on CTR/CVR prediction and multi-objective optimization. My background aligns perfectly with your mission to drive user retention and engagement through intelligent content delivery.
In my previous roles, I have successfully implemented recall and ranking systems that significantly improved long-term user value. I am particularly drawn to this opportunity because it combines the agility of a 0-to-1 venture with the massive scale and resources of AppLovin. I am proficient in PyTorch and Python, and I am eager to apply my expertise in distributed training and A/B testing to help shape the foundation of your new content ecosystem.
Составьте идеальное письмо к вакансии с ИИ-агентом
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Описание вакансии
About AppLovin
AppLovin makes technologies that help businesses of every size connect to their ideal customers. The company provides end-to-end software and AI solutions for businesses to reach, monetize and grow their global audiences. For more information about AppLovin, visit: www.applovin.com.
To deliver on this mission, our global team is composed of team members with life experiences, backgrounds, and perspectives that mirror our developers and customers around the world. At AppLovin, we are intentional about the team and culture we are building, seeking candidates who are outstanding in their own right and also demonstrate their support of others.
Fortune recognizes AppLovin as one of the Best Workplaces in the Bay Area, and the company has been a Certified Great Place to Work for the last four years (2021-2024). Check out the rest of our awards HERE.
【The Role】
We are looking for a Machine Learning Engineer with strong experience in large-scale recommendation systems to help build the next-generation social media platform.
You will own critical components of our recommendation stack — including recall, ranking, CTR modeling, and multi-objective optimization — with the goal of driving retention, engagement, and long-term ecosystem growth.
【A Day in the Life】
- Design and deploy scalable recommendation pipelines
- Develop and optimize CTR/CVR prediction models
- Improve multi-objective ranking strategies (retention, monetization, diversity, long-term value)
- Tackle cold-start challenges for new users and new content
- Run offline experiments and online A/B testing to drive measurable gains
- Collaborate closely with product, engineering, and monetization teams
- Continuously iterate on model performance, latency, and system reliability
【The Impact You’ll Make】
- Improve user retention through intelligent content recommendation
- Drive measurable lift in engagement and monetization metrics
- Build core ranking mechanics beyond incremental model tuning
- Shape the foundation of a scalable, long-term content ecosystem
【Who You Are】
- 3–5 years of experience building production-grade ML systems
- Strong hands-on experience in recommendation systems
- Experience in one or more:
- Recall systems / candidate generation
- Ranking models
- CTR prediction
- Multi-task or multi-objective optimization
- Proficient in Python and ML frameworks (PyTorch, TensorFlow, etc.)
- Strong software engineering fundamentals
- Experience with large-scale data systems and distributed training is a plus
- Experience improving retention or long-term user value is highly valued
【Why Join This Venture】
- Build 0-to-1 systems inside a proven AI powerhouse
- High ownership and direct business impact
- Startup speed with AppLovin-level scale and resources
- Opportunity to shape a new growth engine for the company
AppLovin has become aware of a scam targeting jobseekers with fake “app optimization” and similar roles. We do not ask our candidates to download apps or make any form of payment(s). AppLovin works with applicants through our Careers page and applovin.com email addresses. If you are contacted through other unofficial channels (such as WhatsApp or Telegram) or asked to download an app or make a payment, these contacts are not legitimate. Confirm the information here and contact us directly with any questions.
AppLovin is proud to be an equal opportunity employer that is committed to inclusion and diversity. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status, or other legally protected characteristics. Learn more about EEO rights as an applicant here.
If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send us a request at accommodations@applovin.com.
AppLovin will consider for employment all qualified applicants with criminal histories in a manner consistent with applicable law. If you’re applying for a position in California, learn more here.
To support an efficient and fair hiring process, we may use technology-assisted tools, including artificial intelligence (AI), to help identify and evaluate candidates. All hiring decisions are ultimately made by human reviewers.
Please read our Global Applicant Privacy Notice to learn more about how AppLovin processes your personal information.
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Навыки
- Python
- PyTorch
- TensorFlow
- Machine Learning
- Recommendation Systems
- CTR Prediction
- Ranking
- A/B Testing
- Distributed Training
- Multi-task Learning
Возможные вопросы на собеседовании
Проверка понимания архитектуры рекомендательных систем.
Как бы вы спроектировали систему отбора кандидатов (recall) для ленты новостей с миллионами единиц контента?
Оценка навыков работы с многофакторной оптимизацией, упомянутой в вакансии.
Как вы подходите к балансировке нескольких целей в ранжировании, например, кликабельности (CTR) и долгосрочного удержания (retention)?
Рекомендательные системы часто сталкиваются с проблемой новых данных.
Какие стратегии вы используете для решения проблемы 'холодного старта' для новых пользователей и нового контента?
Проверка инженерных навыков и понимания производительности.
Как вы оптимизируете задержку (latency) при инференсе сложных моделей ранжирования в реальном времени?
Важно понимать, как кандидат оценивает успех своих моделей.
Опишите ваш процесс проведения A/B тестов: какие метрики вы считаете ключевыми и как интерпретируете противоречивые результаты?
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