Staff Engineer, Machine Learning Acceleration
Location: Remote - Global
Job Type: Full time
Shopify’s mission is to make commerce better for everyone, and a core tenet of this mission is to make entrepreneurship easier. We are deeply passionate about the potential of AI to achieve this goal by significantly reducing the toil associated with building and growing a business. At Shopify, we want to bring this technology directly into the hands of merchants, and we need your help.
The surface area that ML/AI can be applied to at Shopify is vast. We have MLEs embedded across many different product and service lines, focussed on delivering enhancements and solving problems. Our infrastructure is vital for rolling out machine learning models because it ensures scalability, performance, availability, reliability, security, and efficient management of the models' lifecycle.
This role would involve supporting priority projects by providing ML infra and engineering expertise to the teams building these models. This helps unlock the full potential of our machine learning initiatives so that we can deliver impactful solutions to our merchants and their customers.
If you thrive on change and are constantly seeking growth and higher mastery, then this is the right place for you. At Shopify, we operate on low process and high trust, and we're not afraid to step out of our comfort zones to push the boundaries of what's possible. So if you're ready to join our team of driven crafters bringing the transformative power of AI to commerce, then we want to hear from you.
If any of the above areas appeal, we’d love to hear from you.
- Developing and deploying end-to-end machine learning systems at scale
- Producing system design and architecture of scalable AI/ML systems
- Solving high-impact data problems and delivering business impact through data and machine learning products
- Working with cross-functional teams including product management, software engineers, and data scientists
- End-to-end experience in training, evaluating, testing, and deploying machine learning products using common ML platforms and frameworks, such as Google VertexAI, Azure ML, AWS Sagemaker, Ray, or equivalent
- Experience with commonly used data engineering toolsets:
- Data Pipelines: Proficient in working with both streaming and batch data processing systems, such as Apache Spark, Flink, or Beam
- Data Modeling Frameworks: Familiarity with data modeling frameworks, like dbt and Airflow
- Data Stores: Knowledge of various data storage systems including vector, analytical, and NoSQL databases. Familiarity with tools like Cassandra, MongoDB, or Elasticsearch for different data storage and retrieval needs
- Experience with running machine learning at scale:
- Operating in distributed infrastructure with Kubernates, GPU optimization, etc
- Using libraries/frameworks, like Tensorflow, Pytorch, scikit-learn, and XGBoost
- Productionizing machine learning models, especially with various computer vision and large language models
- Extensive experience using Python including a strong grasp of object-oriented programming (OOP) fundamentals
It would be great if you have
- Built search-related products e.g. chatbots, search ranking/relevance algorithms, personalization and recommender systems
- Experience with statistical methods like regression, GLMs or A/B experiment design and analysis, other advanced techniques are also welcome
- Exposure to other languages such as JVM based Java / Scala, or scripting languages like Ruby, Typescript, etc
All your information will be kept confidential according to EEO guidelines.
Closing date: Monday October 2nd at 11:59PM EDT.