Senior Software Engineer - Machine Learning Pipelines

$150,000 - $200,000/Yr

RWE - Bellevue, WA

Full-time - Senior

Hybrid - Bellevue, WA

1,001-5,000 employees

Specialty Trade Contractors

About the position

We are building our core AI Weather Lab team to explore creative applications of AI methods to challenges in the renewable energy industry. To help us innovate and build industry-leading AI models, we are seeking an experienced senior software engineer with a proven track record in designing and implementing production-grade pipelines for machine learning models. As a leading member of our engineering team, you will play a pivotal role in architecting, developing, and optimizing end-to-end pipelines that enable the deployment and scaling of advanced machine learning solutions in real-world applications. The successful candidate will play a critical role in developing and maintaining the tech stack of the lab and will work with senior scientists and engineers from a variety of fields. RWE is the third-largest producer of wind power in the world, so you will have a material impact on renewable energy production across the globe. If you're passionate about building scalable pipelines for state-of-the-art production machine learning models and thrive in a fast-paced, collaborative environment, we'd love to hear from you!

Responsibilities

• Lead the design, development, and operation of complex machine learning and multi-faceted data fusion pipelines in a production environment.
• Collaborate closely with data scientists, machine learning engineers, and scientists to integrate machine learning models into scalable production systems.
• Develop and oversee reusable components, libraries, and frameworks to streamline pipeline development and maintenance.
• Optimize pipeline performance, reliability, and resource utilization.
• Stay on the forefront of advancements in machine learning technologies, cloud services, and software engineering best practices with an eye towards identifying and integrating exceptional new components into the AI lab's production repertoire.
• Foster a strong team culture with diverse viewpoints, backgrounds, and expertise.

Requirements

• 7+ years of experience building, deploying, and monitoring complex pipelines that operate at an hours-level cadence.
• Extensive experience in large scale data processing and distributed systems such as Apache Spark.
• Extensive experience in serverless computing and event-driven architectures for building scalable and cost-effective pipelines, including stream processing frameworks for real-time data ingestion and processing.
• Deep expertise in cloud computing platforms such as AWS, Azure, or Google Cloud Platform.
• Strong understanding of containerization technologies (Docker, Kubernetes) and orchestration tools.
• Proficiency with version control systems (e.g., Git), continuous integration/continuous deployment (CI/CD) pipelines, and infrastructure-as-code tools.
• Proficiency in Python and SQL.
• Proficiency in database technologies for data storage and retrieval (e.g., PostgreSQL, MongoDB, BigQuery).
• Excellent problem-solving skills, attention to detail, and a passion for learning and innovation.
• Strong communication and leadership skills, with the ability to work effectively in cross-functional teams and contribute to a positive team climate.

Nice-to-haves

• Experience with Google Cloud Platform MLOps tools.
• Familiarity with monitoring, logging, and debugging tools for distributed systems (e.g., Prometheus, ELK Stack).
• Understanding of security best practices for securing data pipelines.
• Expertise working with large geospatial weather and climate datasets as well as the Pangeo stack.

Benefits

• Dynamic of a start-up in a well-established environment.
• Using cutting-edge technology to solve challenging matters.
• Contribution to the company goals of energy transition.
• Hybrid working model with 3 days in the office to foster collaboration and teamwork.
• Diverse and multicultural team in a highly dynamic and rapidly growing business.