Harvey Staff Software Engineer on Model Infrastructure team leading design and development of systems powering AI requests at Harvey. Responsible for building scalable, reliable, and observable AI inference infrastructure.
Responsibilities
As a Staff Software Engineer on the Model Infrastructure team, you'll lead the design and development of the systems that power every AI request at Harvey. You'll partner closely with AI Research, Product Engineering, Infrastructure, and external model providers to build a platform that is highly reliable, scalable, observable, and efficient.
Lead the design and implementation of Harvey's Model Infrastructure platform.
Build systems to ensure high availability, low latency, and operational excellence for AI inference.
Design and improve Harvey's Unified Model Controller (UMC) and Model Selector platform to automatically detect model degradations and intelligently route traffic based on reliability, latency, quality, compliance, and cost.
Develop systems for model provisioning, capacity management, failover, and traffic engineering across multiple AI providers.
Integrate new model providers and maintain provider APIs and SDKs, enabling Harvey to rapidly adopt emerging frontier models.
Improve observability through health dashboards, alerting, token usage analytics, cost reporting, and end-to-end telemetry.
Partner with Product Engineering to support model launches, experimentation, and proactive monitoring of production AI workloads.
Drive infrastructure efficiency through capacity planning, utilization optimization, and cost visibility.
Collaborate with AI Research to build the infrastructure foundation for future model evaluation, training, and deployment.
Lead cross-functional technical initiatives and mentor engineers across the organization.
What You’ll Build
Qualification
Experience with AI infrastructureExperience with model routingExperience working with OpenAIExperience with Kubernetes
Preferred
Experience with AI infrastructure, LLM serving, or machine learning platforms.
Experience with model routing, inference gateways, or policy-based serving systems.
Experience working with OpenAI, Anthropic, Azure OpenAI, Fireworks, Baseten, or open-source LLMs.
Experience with Kubernetes, cloud infrastructure, and service mesh technologies.
Experience with large-scale observability and SRE best practices.
Experience with data infrastructure technologies such as Kafka, Spark, Flink, Airflow, or Iceberg.
Familiarity with GPU infrastructure or model training platforms.
