About the Role
Blockchain.com is seeking a Machine Learning Engineer to join our Data Science and Business Intelligence team. Data exploitation is central to our business, and in this role, you will play a crucial part in developing and deploying ML Infrastructure to enable world-class user experiences across all our products. You will support the organization in various areas including experimentation, fraud detection, market signals, marketing, and pricing.
Responsibilities
Entry-Level
- Develop and deploy ML Infrastructure, including feature store, data and model version control, training pipelines, inference serving, logging, and scaling systems.
- Consistently advance the state of ML for your problem domain, setting and executing against roadmaps.
- Define projects for other engineers.
- Own the full ML life cycle for significant new ML products, including production quality and continuous improvements.
- Complement data scientists by contributing to a reliable, secure, and maintainable modeling framework for production model deployment.
- Advocate for ML excellence.
- Code deliverables in tandem with Data Scientists.
Senior
- Consistently advance the state of ML for your problem, including setting and executing against roadmaps for 6-month+ timeframes.
- Define projects for other engineers to solve and achieve impact based on your direction.
- Own the full ML life cycle for a significant new ML product, including product quality and continued improvements.
- Advocate for ML excellence.
- Code deliverables in tandem with Data Scientists.
- Complement our data scientists by providing a reliable, secure and maintainable modelling framework that can be used to deploy models to production easily.
- Play a critical role in helping to set up directions and goals for the team.
- Build and ship high-quality code, provide thorough code reviews, testing, monitoring and proactive changes to improve stability.
- Implement the hardest part of the system or feature.
Staff
- Consistently advance the state of ML for your problem, including setting and executing against roadmaps for 6-month+ timeframes.
- Complement our data scientists by designing and implementing a reliable, secure and maintainable modelling framework that can be used to deploy models to production easily.
- Define projects for other engineers to possibly solve and achieve impact based on your direction.
- Own the full ML life cycle for a significant new ML product, including production quality.
- Advocate for ML excellence.
- Code deliverables in tandem with Data Scientists.
- Play a critical role in helping to set up directions and goals for the team.
- Build and ship high-quality code, provide thorough code reviews, testing, monitoring and proactive changes to improve stability.
- Implement the hardest part of the system or feature.
Requirements
Entry-Level
- Experience with developing end-to-end machine learning pipelines that ensure consistency between development and production environments.
- Ability to design ML architectures for scale with site traffic and complexity of features for predictive algorithms.
- Care with regards to model and data versioning, resource allocation and scaling, and logging to build optimal systems.
- Experience with creating systems that monitor and react to faults in resources, data streams and model responses.
Senior
- Ability to lead/coordinate rollout and releases of major initiatives.
- Experience with developing end-to-end machine learning pipelines that ensure consistency between development and production environments.
- Experience working with distributed storage systems.
- Ability to design ML architectures for scale with site traffic and complexity of features for predictive algorithms.
- Care with regards to model and data versioning, resource allocation and scaling, and logging to build optimal systems.
- Experience with creating systems that monitor and react to faults in resources, data streams and model responses.
- Experience with MLOps tools for scalable, production-level deployment including past work with feature stores, model hosting and versioning, data versioning, prediction and drift monitoring, and automated remediation.
Staff
- Ability to solve technical problems that few others can do.
- Ability to lead/coordinate rollout and releases of major initiatives.
- Experience with developing end-to-end machine learning pipelines that ensure consistency between development and production environments.
- Experience working with distributed storage systems.
- Ability to design ML architectures for scale with site traffic and complexity of features for predictive algorithms.
- Care with regards to model and data versioning, resource allocation and scaling, and logging to build optimal systems.
- Experience with creating systems that monitor and react to faults in resources, data streams and model responses.
- Deep experience with MLOps tools for scalable, production-level deployment including past work with feature stores, model hosting and versioning, data versioning, prediction and drift monitoring, and automated remediation.
Nice to Have
- Experience with Airflow or Google Composer.
- Experience with Python and other programming languages such as Java, Kotlin or Scala.
- Experience with Spark or other Big Data frameworks.
- Experience with Kubernetes for data and ML workloads.
- Experience working with open-source machine learning libraries.
- Experience with commonly used ML Libraries: Xgboost, lgbm, sklearn.