AI Engineer

This is a hands-on AI Engineer role based in Sunnyvale focusing on building, optimizing, and shipping machine learning models into production. If you enjoy the messy reality of data pipelines and model deployment rather than just writing Jupyter notebooks, this gig will keep you on your toes.

What You'll Actually Be Doing

You will spend your days bridging the gap between raw data and production-ready intelligence. Expect to spend a solid chunk of time cleaning messy datasets, setting up training loops, and figuring out why your model's latency spikes when hitting cloud infrastructure. You'll work alongside software engineers to refactor prototype code into robust, scalable systems that can handle real-world traffic without crashing.

The Core Tech Stack

You need to be fluent in Python and comfortable leveraging modern frameworks like PyTorch or TensorFlow. Beyond the modeling basics, cloud familiarity with AWS, Azure, or GCP is non-negotiable because your models have to live somewhere scalable. Solid grasp of data structures and algorithms is assumed since you will be optimizing performance for heavy workloads.

Interview Expectations

Expect the hiring manager to throw a system design question at you, such as how you would deploy a heavy computer vision or NLP model to handle high-concurrency requests with minimal latency. They are secretly testing whether you understand production constraints like memory footprints and API rate limits, not just textbook ML theory. Be ready to defend your choice of evaluation metrics on an imbalanced dataset.

Application Advice

Skip the generic tech buzzwords and make sure your resume explicitly highlights end-to-end ML projects where you pushed code to production, not just trained models locally. Drop specific keywords like PyTorch, AWS, data pipelines, and model optimization directly into your experience bullets to clear the initial ATS filters.