Senior Software Engineer, Machine Learning, Reliability Infrastructure

In this role, you'll:

• Report to the Model Training and Inference Reliability TLM
• Design and implement scalable, reliable, and efficient ML infrastructure and pipelines.
• Collaborate with data scientists, engineers, and other partners to deploy machine learning models into production.
• Develop CI/CD pipelines for automating data generation, model training, testing, and deployment processes.
• Monitor and optimize model performance, scalability, and reliability in production environments.
• Implement best practices for model versioning, monitoring, and governance.
• Troubleshoot and resolve issues related to model deployment and performance.
• Stay current with emerging technologies and trends in ML Ops and DevOps domains.
• Be aware of best practices used in the Alphabet stack of ML technologies (e.g. TF, JAX, Flax, or Beam).

At a minimum, we'd like you to have:

• B.S. in Computer Science or Math, or equivalent real-world experience
• 5+ years building and maintaining high-scale distributed or ML inference systems
• Code and testing skills, specifically Python/C++
• Familiarity with large-scale fleet management, testing, and deployment (e.g. GCP/AWS/Kubernetes, Jenkins, GCS/S3)

It's preferred if you have:

• Previous experience deploying and supporting machine learning models for computer vision, natural language processing, or recommendation systems
• Understanding of machine learning fundamentals and experience with popular ML frameworks such as JAX, PyTorch, or Tensorflow

#LI-Hybrid