Software Engineer, ML Platform

Cursor; Palo Alto (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Cursor is seeking an experienced platform infrastructure engineer in Palo Alto to design, build, and operate scalable systems used by ML researchers and product engineers. You will own reliability, performance, and the developer experience across distributed production systems.

You will partner with research teams to turn recurring needs into durable infrastructure, improving the path from product usage and experiments to trusted model runs, with a strong emphasis on Kubernetes or Ray

Qualifications

• Strong systems or infrastructure engineering experience with ownership of production distributed systems at scale.
• Comfortable with Linux, cloud or bare-metal environments, and orchestration tools such as Kubernetes or Ray.
• Ability to work closely with ML researchers and product engineers to turn needs into durable infrastructure.

Responsibilities

• Design, build, and operate platform systems used by ML researchers and product engineers with ownership of reliability, performance, and developer experience.
• Collaborate with research teams to turn recurring needs into durable infrastructure and improve the path from product usage to trusted model runs.

Skills

Distributed Systems

Infrastructure Software Engineering

Production Systems

Event Ingestion

Data Pipelines

Scheduling And Orchestration

Linux

Cloud Computing

Bare-Metal Infrastructure

Kubernetes

Ray

GPU Cluster Scheduling

Observability

OpenTelemetry

Spark

Flink

Tools

Kubernetes

Ray

Job description

Design, build, and operate platform systems used by ML researchers and product engineers, with ownership of reliability, performance, and developer experience. Partner with research teams to turn recurring needs into durable infrastructure and improve the path from product usage and research experiments to trusted model runs.

Requirements:

Candidates should have strong systems or infrastructure engineering experience and have owned production distributed systems at meaningful scale, such as ingestion, data pipelines, or scheduling systems. They should be comfortable with Linux, cloud or bare-metal environments, and orchestration tools such as Kubernetes or Ray, and enjoy working closely with ML researchers and product engineers.

Key Skills:

Distributed Systems, Infrastructure Software Engineering, Production Systems, Event Ingestion, Data Pipelines, Scheduling And Orchestration, Linux, Cloud Computing, Bare-Metal Infrastructure, Kubernetes, Ray, GPU Cluster Scheduling, Observability, OpenTelemetry, Spark, Flink