Rebel Space is seeking a talented and experienced Machine Learning Engineer to join our growing technical team.

About Rebel Space:

At Rebel Space, our mission is to protect critical space infrastructure through enhanced observability and space system cybersecurity. We believe that as space infrastructure expands, it will be increasingly difficult to secure and monitor these systems against critical failures or evolving cyber threats. To address this, we are building software that empowers developers and operators to rigorously evaluate and secure their systems from conception through to operations. We supercharge the way space infrastructure is tested, monitored, and secured, ensuring systems are safeguarded in an increasingly complex space environment. Join us in building the space security infrastructure of the future.

Our view is that the convergence of artificial intelligence, software, robotics, and communications will significantly improve how we connect with physical hardware and digital systems. By focusing on the integration of autonomy with space system cybersecurity, we hope to make physical and digital infrastructure more resilient in the face of a rapidly changing world.

We value experimentation and seek to actively create a positive change through technological innovation. We work in a collaborative environment where new ideas can be shared and explored respectfully and promote a workplace that enhances, not overwhelms, the lives of our team members. We are based out of Long Beach, California and operate on a hybrid schedule.

The Role:

As a Full Stack Software Engineer at Rebel Space, you will work with our engineering team to develop high quality prototypes by applying research, design, and engineering best practices.

Responsibilities:

• Define and own system-wide data architecture that integrates data components across the Rebel Space autonomy stack for enhanced analysis and insights.
• Research, prototype, and survey different ML architecture and workflow optimization techniques (e.g., Neural Architecture Search, Auto-ML)
• Develop proofs-of-concept of customized optimizations that demonstrate the benefit of your optimizations on real-world models using real-world datasets.
• Develop data collections, labeling pipelines, and evaluation pipelines. Research and develop machine learning models for environmental and RF sensor resources.
• Extend existing ML libraries and frameworks.
• Create and deliver reliable software through requirements generation, continuous integration, automated testing, issue tracking, and code reviews.
• Own technical projects from start to finish and be responsible for major technical decisions and tradeoffs. Effectively participate in team planning, code reviews, and design discussions.