Software Engineer, ML/Data Engineering

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

Rakuten Rewards is looking for a Software Engineer with the background of data engineering and is keen to learn machine learning, who is eager to solve data engineering challenges to join our team. We’re a distributed team across the US responsible for building end-to-end production grade pipelines etc and machine learning systems used by millions of users. If you have the software engineering background who is interested in creating / managing data pipelines and wants to gain experience in this field, this is a great place to be. As part of the Rakuten Rewards team, you’ll experience the best of both worlds, a large profitable multinational company with the rapid growth and agility of a startup. Working in a Data Mesh structure, the position will be with one of the data groups that is responsible for developing data products that will help us with our engagement marketing efforts, sale forecasts, and relevancy to fulfill the needs of our merchant partners and members alike. We strive to build the best technology that fits our business needs

KEY RESPONSIBILITIES

• Collaborate with Data Scientists and business stakeholders to apply data pipelines and ML to solve real world problems. The framework and tools that we are currently using are: Scikit-Learn, xgBoost, Pytorch, Pytorch Lightning, Prophet, Gensim, DVC, Ray, BentoML.
• Collaborate with data scientists, developers, and product managers to productionize ML solutions end to end in a highly scalable environment.
• Design, develop, deploy, improve, and maintain the end-to-end machine learning and deep learning systems from data collection, feature engineering, model training, model testing, deployment, and monitoring, in both batch and real-time fashion.
• Develop, improve, and maintain automated data pipelines. We use Snowflake, Argo, Airflow, AWS EKS, Spark, Python and dbt.
• Champion software engineering best practices.
• Contribute to design and code reviews.
• Understand how to break work down into concise deliverables with a focus on iterative product delivery.
• Own solutions end to end from conception to release by managing complexity and collaboratively working with various stakeholders to achieve desired outcomes.
• Debug and resolve issues across multiple technology stacks and services.

MINIMUM REQUIREMENTS (Knowledge, Skills, Abilities)

• Strong computer science fundamentals such as algorithms, data structures, databases, distributed computing.
• Proficient in Python, SQL and scientific python ecosystem – numpy, pandas, etc. ·
• Background in Machine Learning and data engineering.
• Have a keen sense of code hygiene: review, documentation, testing, CI/CD.
• Hands-on experience implementing or prototyping machine learning in Python or other languages.
• Thorough knowledge of programming paradigms (multithreading, caching, performance tuning).
• Strong orientation towards data and data driven decisions.

QUALIFICATION REQUIREMENTS

• 1+ years of professional software development experience building large scale data systems.
• Experienced with DevOps practices.
• Experience with MLOps is a plus.
• Experience with Git, AWS, Docker, CI/CD tool such as Github Actions and Terraform.
• Experience with Kubernetes, Helm, etc.
• Experience with Pytorch and/or Pytorch Lightning.
• Experience with different kind of databases: Relational DB, document-based DB, KV store, columnar based DB, graph DB, etc.
• Experience with different software architectures such as event-based architectures and microservice architectures.