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Senior Software Engineer, Machine Learning
Roku — лидер рынка стриминга с уникальной инженерной культурой. Позиция предлагает работу над сложными наукоемкими задачами в Кембридже, одном из главных технологических хабов Европы, с хорошим соцпакетом и гибридным графиком.
Сложность вакансии
Высокая сложность обусловлена требованием степени PhD, наличием научных публикаций и необходимостью глубоких знаний в области SOTA Deep Learning и генеративных моделей. Роль предполагает лидерство в проведении прикладных исследований и их внедрении в высоконагруженные системы.
Анализ зарплаты
Зарплата для Senior ML ролей в Кембридже обычно выше среднего по рынку Великобритании из-за высокой концентрации талантов и сложности задач. Roku, как публичная американская компания, обычно предлагает конкурентоспособные пакеты, включающие базовый оклад и акции (RSU).
Сопроводительное письмо
I am writing to express my strong interest in the Senior Software Engineer, Machine Learning position at Roku. With a solid background in developing state-of-the-art deep learning models and a passion for solving complex optimization problems, I am eager to contribute to the Advertising Performance group. My experience in discriminative and generative modeling aligns perfectly with Roku's mission to enhance the TV streaming ecosystem through innovative advertising solutions.
Throughout my career, I have focused on bridging the gap between academic research and scalable production systems. I am particularly drawn to this role because of the opportunity to work on multi-objective optimization and real-time inference at scale. Having published research in related fields and possessing a deep understanding of statistical techniques, I am confident in my ability to lead technical initiatives that drive yield and margin optimization while delivering a superior experience for streamers.
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Откликнитесь в roku уже сейчас
Присоединяйтесь к Roku в Кембридже и создавайте будущее рекламных технологий на базе SOTA моделей машинного обучения!
Описание вакансии
Teamwork makes the stream work.
Roku is changing how the world watches TV
Roku is the #1 TV streaming platform in the U.S., Canada, and Mexico, and we've set our sights on powering every television in the world. Roku pioneered streaming to the TV. Our mission is to be the TV streaming platform that connects the entire TV ecosystem. We connect consumers to the content they love, enable content publishers to build and monetize large audiences, and provide advertisers unique capabilities to engage consumers.
From your first day at Roku, you'll make a valuable - and valued - contribution. We're a fast-growing public company where no one is a bystander. We offer you the opportunity to delight millions of TV streamers around the world while gaining meaningful experience across a variety of disciplines.
About the team
The Advertising Performance group focuses on performance for all participants in the Advertising ecosystem - Advertisers, Publishers and Roku. The systems and solutions span across different disciplines and technologies to perform realtime multi-objective optimization with distributed systems at large scale and low latencies. We use Machine Learning, Reinforcement Learning, AI, Control and Optimization Systems and Auction Dynamics to solve a large set of complex problems. At the core of this is our Machine Learning, Experimentation and Inference Platform that powers the entire landscape which we continuously evolve over time.
About the role
In this role you will work on applying SOTA research and conduct your own research to develop novel methodologies to solve a large variety of challenging problems in Advertising related to conversion modeling aligned with attribution methodologies/models, calibration, dynamic creative generation and optimization, forecasting and timeseries modeling, yield and margin optimization and Experimentation for A/B and multivariate testing.
We’re looking for a strong technical leader with a solid grasp of core statistical techniques and deep experience in SOTA Deep Learning discriminative and generative models.
What you will be doing
- Applying existing and conducting your own research to build SOTA Deep learning discriminative models and build generative models to create image and video ads geared towards optimizing performance.
- Stay at the forefront of advancements in related areas.
We're excited if you have
- PhD in quantitative disciplines such as CS, Statistics, Applied Math or a related field.
- Extensive experience in applied research using statistical and deep learning techniques.
- Published paper(s) on deep learning models for Advertising or related areas.
- Excellent communication and collaboration skills.
- Experience in the Advertising domain (preferred)
- Contributions to open-source ML projects (preferred)
Our Hybrid Work Approach
Roku fosters an inclusive and collaborative environment where teams work in the office Monday through Thursday. Fridays are flexible for remote work except for employees whose roles are required to be in the office five days a week or employees who are in offices with a five day in office policy.
Benefits
Roku is committed to offering a diverse range of benefits as part of our compensation package to support our employees and their families. Our comprehensive benefits include global access to mental health and financial wellness support and resources. Local benefits include statutory and voluntary benefits which may include healthcare (medical, dental, and vision), life, accident, disability, commuter, and retirement options (401(k)/pension). Our employees can take time off work for vacation and other personal reasons to balance their evolving work and life needs. It's important to note that not every benefit is available in all locations or for every role. For details specific to your location, please consult with your recruiter.
Accommodations
Roku welcomes applicants of all backgrounds and provides reasonable accommodations and adjustments in accordance with applicable law. If you require reasonable accommodation at any point in the hiring process, please direct your inquiries to EmployeeRelations@Roku.com.
The Roku Culture
Roku is a great place for people who want to work in a fast-paced environment where everyone is focused on the company's success rather than their own. We try to surround ourselves with people who are great at their jobs, who are easy to work with, and who keep their egos in check. We appreciate a sense of humor. We believe a fewer number of very talented folks can do more for less cost than a larger number of less talented teams. We're independent thinkers with big ideas who act boldly, move fast and accomplish extraordinary things through collaboration and trust. In short, at Roku you'll be part of a company that's changing how the world watches TV.
We have a unique culture that we are proud of. We think of ourselves primarily as problem-solvers, which itself is a two-part idea. We come up with the solution, but the solution isn't real until it is built and delivered to the customer. That penchant for action gives us a pragmatic approach to innovation, one that has served us well since 2002.
To learn more about Roku, our global footprint, and how we've grown, visit https://www.weareroku.com/factsheet.
By providing your information, you acknowledge that you want Roku to contact you about job roles, that you have read Roku's Applicant Privacy Notice, and understand that Roku will use your information as described in that notice. If you do not wish to receive any communications from Roku regarding this role or similar roles in the future, you may unsubscribe at any time by emailing WorkforcePrivacy@Roku.com.
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Навыки
- Machine Learning
- Deep Learning
- Reinforcement Learning
- Generative AI
- Statistics
- Python
- A/B Testing
- Distributed Systems
- Optimization
Возможные вопросы на собеседовании
Вакансия требует опыта в SOTA Deep Learning. Важно понять, как кандидат переносит теорию в практику.
Расскажите о вашем опыте внедрения SOTA моделей (например, трансформеров или диффузионных моделей) в продакшн-среду с низким временем отклика.
Группа занимается оптимизацией рекламы. Вопрос проверяет понимание специфики домена.
Как бы вы подошли к решению проблемы калибровки предсказаний CTR/CVR в условиях динамически меняющегося аукциона?
Упоминается использование генеративных моделей для создания рекламы.
Какие архитектурные подходы вы бы использовали для автоматической генерации видео-креативов, оптимизированных под конкретные метрики эффективности?
Роль требует сильных статистических навыков.
Как вы оцениваете инкрементальный эффект (incrementality) в рекламных кампаниях при наличии множества смещающих факторов?
Работа в Roku предполагает кросс-функциональное взаимодействие.
Опишите случай, когда вам приходилось объяснять сложные концепции машинного обучения стейкхолдерам, не обладающим техническими знаниями.
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