- Страна
- Канада
- Зарплата
- 104 400 ₽ – 176 040 ₽
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Software Engineer II, Machine Learning
Отличная позиция в известной международной компании с прозрачной вилкой зарплаты и сильной инженерной культурой. Использование современных технологий (GCP, Kubernetes, Reinforcement Learning) делает роль привлекательной для профессионального роста.
Сложность вакансии
Роль требует уверенного владения Python и опыта вывода ML-моделей в продакшн (MLOps), а не просто работы в Jupyter-ноутбуках. Кандидат должен разбираться в современной инфраструктуре (Kubernetes, Airflow) и принципах чистого кода.
Анализ зарплаты
Предлагаемая зарплата (104k - 176k CAD) полностью соответствует и даже немного превышает рыночные стандарты для уровня Software Engineer II в Торонто. Верхняя граница диапазона с учетом бонусов (OTE до 195k) является очень конкурентоспособной для канадского рынка.
Сопроводительное письмо
I am writing to express my strong interest in the Software Engineer II (Machine Learning) position at Braze. With over two years of experience in Python development and a proven track record of deploying ML models into production environments, I am excited about the opportunity to contribute to your self-learning AI platform. My background in building robust data pipelines using PySpark and SQL, combined with my experience in cloud architectures like GCP, aligns perfectly with the technical requirements of your team.
What draws me to Braze is your commitment to engineering excellence—specifically your focus on modular design, CI/CD, and testable APIs. I have always advocated for clean, type-hinted code and have extensive experience working with tools like Airflow and Kubernetes to ensure model reliability. I am eager to bring my "tinkerer" mindset and entrepreneurial spirit to help Braze continue its rapid growth and deliver personalized customer experiences at scale.
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Откликнитесь в braze уже сейчас
Присоединяйтесь к команде Braze, чтобы внедрять передовые ML-решения в реальный продакшн и масштабировать AI-платформу мирового уровня!
Описание вакансии
At Braze, we have found our people. We’re a genuinely approachable, exceptionally kind, and intensely passionate crew.
We seek to ignite that passion by setting high standards, championing teamwork, and creating work-life harmony as we collectively navigate rapid growth on a global scale while striving for greater equity and opportunity – inside and outside our organization.
To flourish here, you must be prepared to set a high bar for yourself and those around you. There is always a way to contribute: Acting with autonomy, having accountability and being open to new perspectives are essential to our continued success.
Our deep curiosity to learn and our eagerness to share diverse passions with others gives us balance and injects a one-of-a-kind vibrancy into our culture.
If you are driven to solve exhilarating challenges and have a bias toward action in the face of change, you will be empowered to make a real impact here, with a sharp and passionate team at your back. If Braze sounds like a place where you can thrive, we can’t wait to meet you.
WHAT YOU’LL DO
Do you enjoy working on data-intensive products? Come join our growing Engineering team to help design, improve and scale Braze's self-learning (reinforcement learning) AI platform. No toy datasets in notebooks - we’re implementing AI pipelines in production at scale! Learn tons about data architecture, data science, and self-learning AI. Work in a team that not only talks-the-talk of development best practices, but walks the walk - unit & integration tests, modular design, CI/CD, pair programming, code reviews - the works.
Responsibilities:
- Use robust software engineering best practices to design, implement, and improve modular components in a cutting-edge ML product
- Work closely with Braze customers to understand, translate and generalize particular use cases to generic platform components
- Apply your extensive knowledge of Python and its ecosystem to produce clean, readable, and extendible code, and coach others on the team in doing the same
- Collaborate with teams responsible for Braze’s product strategy and roadmap
- Support teams implementing Braze for customers to ensure their success
- Data Science/Back End: Python (Pyspark, Polars, Ibis), SQL, BigQuery, FastAPI
- Architecture/DevOps: Kubernetes, Airflow, Terraform, GCP
- We write well-tested, type-hinted, documented, modular code and use pre-commit hooks, CI/CD, and issue tracking for development
WHO YOU ARE
- Exceptional coder: you write clean, object-oriented code; you care about good design and terse, testable APIs
- Tinkerer: you regularly explore and learn new technologies and methods, especially in the data architecture and data science domains
- Entrepreneurial: you proactively identify opportunities and risks, work around obstacles, and always seek creative ways to improve processes and outcomes
- Structured and organized: you can structure a plan, align stakeholders, and see it through to execution
- Clear communicator: you are able to express yourself clearly and persuasively, both in writing and speech
- 2+ years of experience working with Python in a product setting, including 1+ years in a the data/machine learning ecosystem
- Experience working with at least one major cloud platform (GCP, AWS, Azure, etc)
- Experience putting ML models into production
- General understanding of supervised learning principles is a plus
For candidates based in Ontario, the pay range at the start of employment for this position is expected to be between CA$104,400 - CA$176,040/year, with an expected On Target Earnings (OTE) between CA$116,000 - CA$195,600/year (including performance-based or variable compensation (bonus or commission). Your particular offer may vary depending on multiple individual factors, including market location, job-related knowledge, skills, and experience. In addition to cash compensation, Braze offers a comprehensive Total Rewards package that includes, among other things, equity grants of restricted stock units (RSUs), so that all Braze employees own a piece of our company.
WHAT WE OFFER
Braze benefits vary by location, and we encourage you to review our specific benefits offerings for each country here. More details on benefits plans will be provided if you receive an offer of employment.
From offering comprehensive benefits to fostering hybrid ways of working, we’ve got you covered so you can prioritize work-life harmony. Braze offers benefits such as:
- Competitive compensation that may include equity
- Retirement and Employee Stock Purchase Plans
- Flexible paid time off
- Comprehensive benefit plans covering medical, dental, vision, life, and disability
- Family services that include fertility benefits and equal paid parental leave
- Professional development supported by formal career pathing, learning platforms, and a yearly learning stipend
- A curated in-office employee experience, designed to foster community, team connections, and innovation
- Opportunities to give back to your community, including an annual company-wide Volunteer Week and donation matching
- Employee Resource Groups that provide supportive communities within Braze
- Collaborative, transparent, and fun culture recognized as a Great Place to Work®
ABOUT BRAZEBraze is the leading customer engagement platform that empowers brands to Be Absolutely Engaging.™ Braze helps brands deliver great customer experiences that drive value both for consumers and for their businesses. Built on a foundation of composable intelligence, BrazeAI™ allows marketers to combine and activate AI agents, models, and features at every touchpoint throughout the Braze Customer Engagement Platform for smarter, faster, and more meaningful customer engagement. From cross-channel messaging and journey orchestration to Al-powered decisioning and optimization, Braze enables companies to turn action into interaction through autonomous, 1:1 personalized experiences.
The company has repeatedly been recognized as a Leader in marketing technology by industry analysts, and was voted a G2 “Best of Marketing and Digital Advertising Software Product” in 2025.
Braze was also named a 2025 Best Companies To Work For by U.S. News & World Report, a 2025 America’s Greatest Companies by Newsweek, and a 2025 Fortune Best Workplace in Technology™ by Great Place To Work®, among other accolades. Braze is also proudly certified as a Great Place to Work® in the U.S., the UK, Australia, and Singapore.
The company is headquartered in New York with offices in Austin, Berlin, Bucharest, Chicago, Dubai, Jakarta, London, Paris, San Francisco, São Paulo, Singapore, Seoul, Sydney and Tokyo.
BRAZE IS AN EQUAL OPPORTUNITY EMPLOYER
At Braze, we strive to create equitable growth and opportunities inside and outside the organization.
Building meaningful connections is at the heart of everything we do, and that includes our recruiting practices. We're committed to offering all candidates a fair, accessible, and inclusive experience – regardless of age, color, disability, gender identity, marital status, maternity, national origin, pregnancy, race, religion, sex, sexual orientation, or status as a protected veteran. When applying and interviewing with Braze, we want you to feel comfortable showcasing what makes you you.
We know that sometimes different circumstances can lead talented people to hesitate to apply for a role unless they meet 100% of the criteria. If this sounds familiar, we encourage you to apply, as we’d love to meet you.
Please see ourCandidate Privacy Policy for more information on how Braze processes your personal information during the recruitment process and, if applicable based on your location, how you can exercise any privacy rights.
Создайте идеальное резюме с помощью ИИ-агента

Навыки
- Python
- Terraform
- Machine Learning
- SQL
- Kubernetes
- CI/CD
- Google Cloud Platform
- BigQuery
- PySpark
- Airflow
- Polars
- FastAPI
- Ibis
- Reinforcement Learning
Возможные вопросы на собеседовании
Проверка практического опыта работы с данными в продакшн-среде.
Расскажите о самом сложном ML-конвейере (pipeline), который вы внедряли: с какими проблемами масштабируемости вы столкнулись?
Вакансия делает упор на качество кода и архитектуру.
Как вы подходите к тестированию ML-кода и обеспечению его модульности, чтобы избежать превращения проекта в 'спагетти-код'?
В стеке указаны PySpark и Polars.
В каких ситуациях вы предпочтете использовать Polars вместо PySpark, и как это повлияет на производительность обработки данных?
Braze использует Airflow и Kubernetes.
Опишите ваш опыт работы с оркестрацией задач. Как вы обрабатываете сбои в рабочих процессах Airflow при обучении моделей?
Позиция подразумевает работу с клиентами и продуктовыми командами.
Как вы переводите абстрактные бизнес-требования заказчика в конкретные технические компоненты ML-платформы?
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- Страна
- Канада
- Зарплата
- 104 400 ₽ – 176 040 ₽