Software Engineer, Inference

About the Role

Sierra’s AI agents depend on foundation models to reason and act in real time. The Inference team builds the systems that make those models fast, reliable, and efficient at scale.

As a Software Engineer on Inference, you’ll help define Sierra’s inference architecture across both self-hosted models and third-party inference providers. You’ll work on the systems responsible for serving and routing inference, managing capacity and quota, and optimizing for latency, reliability, and cost.

This is a systems-first role at the intersection of distributed infrastructure and AI. You don’t need to be an ML researcher—we’re looking for engineers who love complex systems problems and are excited to apply that expertise to one of the fastest-moving areas of AI infrastructure.

What You'll Do

• Partner with frontier labs and providers: Supply capacity, training, and inference infrastructure at scale.
• Shape Sierra’s inference architecture: Design how inference traffic flows across models, infrastructure, and providers, including new serving and proxy layers as Sierra scales.
• Build for low latency and high reliability: Develop systems for routing, failover, capacity management, and quota that keep inference performant and available across large-scale production workloads.
• Build and operate self-hosted inference: Run models on GPU infrastructure, from building containers and operating inference engines to managing compute capacity.
• Optimize inference performance: Work with the Applied Research team on techniques such as speculative decoding and serving-engine optimizations to improve latency, throughput, and cost.
• Build across a hybrid inference stack: Work with both Sierra-managed infrastructure and leading inference platforms, making architectural decisions about where and how workloads run.
• Push the serving stack forward: Work closely with inference providers to tune engines and infrastructure for Sierra’s workloads.
• Support the broader model lifecycle: Contribute to infrastructure that enables post-training while partnering closely with Models and Agent Runtime teams.

What You'll Bring

• Deep systems thinking and strong distributed systems fundamentals.
• Experience designing, building, and operating large-scale production systems.
• Strong judgment around tradeoffs involving latency, reliability, capacity, and cost.
• Experience taking ownership of complex infrastructure from architecture through production operation.
• Excitement about applying systems expertise to AI infrastructure and learning quickly as underlying technology evolves.

Even Better

• Experience with ML infrastructure, MLOps, or production inference systems.
• Experience serving LLMs or other large models at scale.
• Experience operating self-hosted inference and GPU infrastructure.
• Familiarity with inference frameworks such as vLLM or SGLang.
• Experience with post-training infrastructure or inference-performance optimization.