Software Engineer - Networking Software and Services

About the role

This role operates within the small nssAI team, applying hands-on engineering to network software and services. You will build tools that enable Network Development Engineers and directly support SpaceXAI mission objectives. The position requires comfort with ambiguity and a drive to deliver reliable automation. You will analyze complex network behaviors and translate them into robust software solutions that keep our infrastructure moving. The work demands ownership of the full lifecycle from design through deployment and incident response in fast-paced environments. You will partner closely with domain experts to ensure network tooling aligns with demanding AI training and inference requirements. Success in this role is defined by your ability to ship dependable features that materially improve network visibility and control.

Key facts

What you'll do

Network topologies and protocols are analyzed to expand metric coverage across GPU supercomputing fabrics used for AI training and inference.

Infrastructure as code practices are implemented to strengthen deployment pipelines and service delivery across production environments.

Network devices are orchestrated at scale through extensible tools that streamline complex management workflows.

Reliability is ensured for network automation solutions that support scientific discovery and customer inference queries.

Metrics frameworks are created to prioritize team focus and clarify ownership in ambiguous problem spaces.

Daily collaboration with network engineers requires deep knowledge of physical and logical network topologies and protocols.

Scalable and reliable software must be designed and built to manage tens of thousands of network devices at high speed.

Ambiguity is navigated by creating metrics that guide team priorities and individual execution.

Hands-on implementation drives direct contribution to production systems and mission outcomes.

Clear communication translates technical details for cross-functional engineering teams during planning and reviews.

You will automate network workflows to reduce manual effort and increase consistency across large-scale operations.

Reliability engineering practices are applied to keep services available for data-intensive AI workloads around the clock.

You will participate in on-call rotations to respond to network incidents and maintain high service integrity.

Testing and validation of network changes are performed rigorously to prevent disruptions in production environments.

Requirements

Daily collaboration with network engineers requires deep knowledge of physical and logical network topologies and protocols.

Scalable and reliable software must be designed and built to manage tens of thousands of network devices at high speed.

Ambiguity is navigated by creating metrics that guide team priorities and individual execution.

A Bachelor's degree or equivalent experience is required for this role.

You must be comfortable working in fast-paced environments with evolving requirements and shifting priorities.

Strong ownership is expected, with the ability to drive projects from initial design through deployment and post-launch improvements.

Experience with network automation and infrastructure as code is essential for success in this position.

The ability to explain technical decisions in clear, concise language is necessary to influence stakeholders and peers.

Practical notes

SpaceXAI is an equal opportunity employer. Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

Network engineering tools support large-scale infrastructure management in high-performance environments.

Automation frameworks reduce manual effort and improve consistency across network operations.

Reliability engineering practices keep services available for data-intensive AI workloads.

Clear communication translates technical details for cross-functional engineering teams.

Hands-on implementation drives direct contribution to production systems and mission outcomes.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.