Requirements

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience in software development.
• 2 years of experience in deploying and maintaining machine learning models in a live production environment.
• Experience in profiling, configuring, or executing ML workloads directly on hardware accelerators (e.g., GPU or TPU).
• Experience designing, building, or optimizing model serving infrastructure or inference backends.

Nice to haves

• Experience with developing serving infrastructure.
• Experience programming hardware accelerators (GPUs, TPUs) via ML frameworks (e.g., JAX, PyTorch) or low-level programming models (e.g., Pallas, CUDA, OpenCL).
• Experience profiling software to identify performance bottlenecks.
• Experience with distributed ML systems optimization and parallelism (e.g., data, model, or pipeline parallelism).
• Familiarity with writing performance-optimized kernels.
• Understanding of LLM architecture and inference performance dynamics (e.g., Transformer models, memory bandwidth and compute bounds, KV cache scaling).

What you'll be doing

• Collaborate closely with Research teams to understand next-generation modeling approaches, ensuring they are designed and implemented with production considerations in mind.
• Work with infrastructure teams to deliver serving infrastructure that is designed for maximum efficiency and performance, addressing bottlenecks in speed, scale, and quality.
• Identify opportunities to automate tasks, eliminate redundancies, build performant tests, and improve the overall velocity of model releases.
• Gain a deep understanding of serving frameworks, pre-processing pipelines, caching mechanisms, and other relevant technologies.
• Leverage roofline analysis, hardware-level profiling, and systems analysis to identify and eliminate performance bottlenecks across ML frameworks, compilers (XLA), custom kernels (Pallas), and serving infrastructure on hardware accelerators (TPUs/GPUs).

Perks and Benefits

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