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Sierra is a leading platform for customer-facing AI agents serving major brands including The GAP, Rocket Mortgage, SoFi, Sutter Health, and SoftBank. The company is founded by Bret Taylor (former Salesforce co-CEO and Facebook CTO) and Clay Bavor (former Google Labs leader).
As a Software Engineer on the Inference team, you will help define Sierra's inference architecture across self-hosted models and third-party inference providers. The Inference team builds systems that make foundation models fast, reliable, and efficient at scale. This is a systems-first role at the intersection of distributed infrastructure and AI.
Key responsibilities include:
- Partnering with frontier labs and inference providers to supply capacity and infrastructure
- Designing how inference traffic flows across models, infrastructure, and providers, including new serving and proxy layers
- Developing systems for routing, failover, capacity management, and quota to keep inference performant and available
- Building and operating self-hosted inference on GPU infrastructure, including containers and inference engines
- Optimizing inference performance through techniques like speculative decoding and serving-engine optimizations
- Working with both Sierra-managed infrastructure and leading inference platforms to make architectural decisions
- Collaborating with inference providers to tune engines for Sierra's workloads
- Contributing to infrastructure that enables post-training while partnering with Models and Agent Runtime teams
The role emphasizes systems thinking and infrastructure expertise applied to AI, not ML research. You'll take ownership of complex infrastructure from architecture through production operation.
REQUIREMENTS:
- 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 technology evolves
PREFERRED QUALIFICATIONS:
- 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
