Software Engineer, ML Platform
Build and operate distributed infrastructure and ML platform primitives that help researchers and product engineers move from experiments to trusted runs on large-scale compute. The role requires production systems experience, strong infrastructure skills, and close collaboration with ML research teams.
About the job
Responsibilities
- Design, build, and operate core platform systems used daily by ML researchers and product engineers.
- Partner with research teams to turn recurring pain points into durable infrastructure.
- Own reliability, performance, and developer experience for assigned systems.
- Ship iteratively in a high-ownership environment, measure impact, and continuously improve platform quality.
Requirements
- Strong background in systems or infrastructure software engineering.
- Experience building platforms that other engineers depend on.
- Experience owning production distributed systems at meaningful scale, such as ingestion systems, data pipelines, or scheduling and orchestration platforms.
- Comfortable working across Linux, cloud and/or bare-metal environments, and modern orchestration systems such as Kubernetes, Ray, or equivalent.
- Enjoy collaborating closely with ML researchers and product engineers.
- Able to thrive in a high-ownership environment with a short feedback loop.
Nice-to-haves
- Event ingestion, product analytics pipelines, OpenTelemetry, tracing, and reliable data APIs.
- Data frameworks, Spark, Flink, Ray, and ML dataset or training-data infrastructure.
- Experiment and run monitoring, debugging and evaluation tooling, and agent-friendly observability.
- GPU and cluster scheduling, job queues, node health, and research-compute developer experience.
Compensation
- Compensation details were not provided.
Skills
Distributed Systems, Infrastructure, Linux, Kubernetes, Ray, Cloud Computing, Gpu Scheduling, Data Pipelines, Spark, Apache Flink, OpenTelemetry, Tracing, Job Queues, Observability
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