LinkedIn, the world's largest professional network, is seeking a Staff Software Engineer to join their Systems and Infrastructure team. This role offers an exciting opportunity to work on next-generation infrastructure and platforms that power LinkedIn's services at massive scale.
The position involves building critical systems including application and service delivery platforms, compute platforms, scalable data storage and replication systems, cutting-edge search platforms, AI platforms, experimentation platforms, and privacy/compliance platforms. LinkedIn has an impressive track record of contributing to the open-source community with projects like Apache Kafka, Pinot, Azkaban, Samza, Venice, Datahub, and Feather.
As a Staff Software Engineer, you'll be working with industry-standard technologies like Kubernetes, GRPC, and GraphQL while having the opportunity to contribute to open-source projects. The role requires strong technical leadership, with responsibilities including owning technical strategy, designing and implementing large-scale distributed systems, improving system observability, and mentoring other engineers.
The position offers competitive compensation ($156,000 - $255,000) and benefits including generous health and wellness programs. The work environment is hybrid, allowing flexibility to work both from home and office locations in Mountain View, San Francisco, or Bellevue.
This is an excellent opportunity for experienced engineers passionate about distributed systems, looking to make a significant impact while working with cutting-edge technologies. The role combines technical leadership, hands-on development, and the chance to contribute to open-source projects that benefit the broader technology community.
LinkedIn's commitment to creating economic opportunity for every member of the global workforce, combined with their investment in employee growth and culture built on trust, care, and inclusion, makes this an attractive position for those looking to work on challenging technical problems at scale while making a meaningful impact.