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Machine Learning Platform Engineer
Отличная вакансия в стабильной логистической компании с современным стеком технологий и четко прописанными бонусами. Офис в престижном районе Гвадалахары и фокус на инженерную культуру делают предложение очень привлекательным.
Сложность вакансии
Роль требует глубоких знаний как в программной инженерии (Kubernetes, микросервисы), так и в специфике ML (feature stores, обслуживание моделей). Необходим опыт работы с высоконагруженными системами и умение работать в гибридном/офисном формате.
Анализ зарплаты
Зарплата в объявлении не указана, но для позиции инженера ML-платформы в Гвадалахаре рыночные показатели обычно выше среднего по региону из-за высокой востребованности навыков MLOps. Предлагаемый соцпакет (ваучеры, страховка, сберегательный фонд) является стандартным для крупных международных IT-хабов в Мексике.
Сопроводительное письмо
I am writing to express my strong interest in the Machine Learning Platform Engineer position at Arrive Logistics. With over 3 years of experience in MLOps and building scalable backend services using Python, I have developed a deep understanding of the entire machine learning lifecycle, from model development with Sklearn and Pandas to deploying containerized applications in Kubernetes. My background in designing distributed systems and event-driven architectures aligns perfectly with your goal of maintaining and expanding Arrive's ML platform capabilities.
In my previous roles, I have successfully implemented model registries and feature stores, ensuring that ML models are served scalably and reliably. I am particularly drawn to Arrive Logistics' collaborative culture and the opportunity to work on ML-powered products that directly impact business profitability. I am excited about the prospect of contributing to your team in Guadalajara and participating in the in-person collaboration that drives innovation at Arrive.
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Откликнитесь в arrivelogistics уже сейчас
Присоединяйтесь к Arrive Logistics в Гвадалахаре и создавайте будущее логистики с помощью передовых ML-технологий!
Описание вакансии
Who We Are
Arrive Logistics is a leading transportation and technology company in North America, with plans to continue to significantly grow year over year. Our success is a testament to our remarkable team and what we are building together. We’re committed to providing employees with a meaningful work experience and have established an award-winning culture that supports personal and career development in a fun, casual, and collaborative environment. There has never been a more exciting time to get on board, so read on to learn more and apply today!
Who We Want
As a Machine Learning Platform Engineer at Arrive Logistics, you will play a key role in maintaining and expanding Arrive's ML platform and product capabilities. This role requires driving and contributing to crucial roadmap projects while ensuring quality, maintainability and functionality. These initiatives may vary from serving models scalably to building ML-powered products focused on maximizing profit.
What You'll Do
- Design, build, and maintain scalable ML systems and infrastructure using Python, Postgres, and Elasticsearch.
- Partner closely with other Machine Learning Engineers, Product Managers, Data Scientists, Data Engineers, and Product Engineers to ensure the successful delivery of strategic and roadmap initiatives
- Own systems throughout the software development lifecycle, from design to development, deployment and monitoring.
- Maintain and improve performance of existing data systems and processes while balancing maintainability, observability and readability.
- Demonstrate a deep sense of ownership by developing a thorough understanding of a domain. At the same time, you must be able to explain the behavior of and contribute to code bases that may be outside your domain.
- Proactively propose solutions to gaps in process, technology, software design and architecture
- Provide rigorous and detailed code reviews that uphold team standards, testing and software design best practices
- Foster a culture of constant improvement and growth, engineering excellence, humility, positivity and curiosity. Take a lead role in making our two days in the office productive and engaging, fostering face-to-face mentorship and collaborative whiteboarding sessions.
Qualifications
- Bachelor’s degree in Computer Science, Engineering, or a related field or equivalent professional experience.
- 3+ years of experience with ML ops (features stores, model registries and auto ML), model serving and optimization.
- 3+ years of experience with Python and building highly scalable backend services
- Experience with frameworks like Sklearn, Pandas and Numpy. Plus if Huggingface, Tensorflow, Pytorch experience.
- 2+ years of experience with relational databases
- 2+ years of experience designing maintainable and scalable systems
- System design experience with a focus on loosely coupled, distributed systems, microservices and event-driven architectures
- Experience developing cloud-native dockerized applications in Kubernetes
- Experience working with online experimentation and platforms like Statsig
- Understanding of both traditional machine learning and deep neural networks
- Strong communication skills with the ability to articulate, diagram and document complex ML or engineering concepts.
- Strong analytical, problem-solving, decision-making, and interpersonal skills.
- Strong project management and organizational skills with experience identifying project milestones to ensure timely project delivery
- You are a self-starter who can deliver projects independently, yet you also thrive in collaborative environments. You recognize the value of diverse perspectives in developing optimal solutions and consistently demonstrate a willingness to support colleagues as a strong team player.
- You approach software engineering as a craft, balancing the pursuit of clean, maintainable code with the demands of a fast-moving, dynamic business environment. You collaborate effectively with product managers and leadership to choose development paths that minimize technical debt while ensuring the timely delivery of high-quality products. While you have a strong drive for innovation, you also recognize the critical need to stabilize and harden existing products and services.
- You believe that while remote work is functional, in-person collaboration is where the "magic" happens. You are excited to help shape the energy of our physical workspace.
- You take initiative to go beyond current responsibilities and actively seek new challenges.
- You are passionate about building high impact ML and data driven products.
The Perks of Working With Us
- Take advantage of our benefits including monthly grocery vouchers, vacation days, savings fund, medical insurance (including dental and vision plans) and more.
- Leave the suit and tie at home; our dress code is casual.
- Enjoy office wide engagement activities, team events, happy hours and more!
- Work in our new Guadalajara office located in Torre 1500 (Av. Americas 1254) within the plaza, you'll find cafes and a wide variety of local restaurants.
- Start your morning with free coffee!
- Maximize your wellness with free counseling sessions through our Employee Assistance Program
- Get paid to work with your friends through our Referral Program!
Your Arrive Experience
Our award-winning company culture is designed with you in mind. We are committed to supporting your personal and professional growth and making Arrive a place we all love to work.
*Notice:*
To ensure a safe and transparent interview process, we want to note that Arrive Logistics adheres to strict recruitment practices. Candidates undergo an interview process, and Arrive Logistics does not provide unsolicited job offers. If you have concerns about receiving a fraudulent offer, please contact talentacquisition@arrivelogistics.com for verification.
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Навыки
- Python
- NumPy
- Pandas
- Kubernetes
- PostgreSQL
- Microservices
- MLOps
- Scikit-learn
- Docker
- Event-Driven Architecture
- ElasticSearch
- Feature Store
- Model Registry
- Statsig
Возможные вопросы на собеседовании
Проверка опыта работы с инфраструктурой для жизненного цикла ML.
Расскажите о вашем опыте внедрения Feature Store или Model Registry: какие инструменты вы использовали и какие проблемы это решило?
Оценка навыков проектирования масштабируемых систем.
Как бы вы спроектировали систему для обслуживания ML-моделей с низкой задержкой при миллионах запросов в день?
Проверка владения современными инструментами контейнеризации.
Опишите ваш опыт развертывания ML-сервисов в Kubernetes. С какими трудностями вы сталкивались при масштабировании подов с моделями?
Оценка понимания специфики данных и баз данных.
В каких случаях для ML-платформы вы бы предпочли Elasticsearch вместо традиционной реляционной базы данных, такой как Postgres?
Проверка навыков командного взаимодействия и код-ревью.
На что вы в первую очередь обращаете внимание при проведении код-ревью в проектах машинного обучения, чтобы обеспечить поддерживаемость кода?
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