Your space-enabled career begins here

LeoLabs is the world’s leading commercial provider of low Earth orbit (LEO) mapping and Space Domain Awareness (SDA) services. LeoLabs was founded in 2016 as a venture-funded spinout of Silicon Valley research pioneer, SRI International, by scientists and space industry veterans committed to securing Low Earth Orbit (LEO). LeoLabs is built on 30+ years of R&D in radar systems and satellite tracking algorithms. The team is rapidly expanding its global radar network and data services platform to help satellite operators deploy their services safely and to empower governmental space agencies with detailed visibility into the LEO ecosystem. As commercial space ventures and newly-formed space agencies from every corner of the globe compete for their place in the second space race, LeoLabs is here to address a new generation of risks and opportunities to preserve LEO for future generations.

Join LeoLabs as an AI Engineer to research and implement applications of AI and ML into an emerging commercial space industry. You will be at the forefront of space technology innovation, working with a team of experts in a field that's vital for the future of global communications, earth observation, and space exploration. Your contributions will not only support our mission but also shape the future of LeoLabs as we expand our services and technologies.

• Explore opportunities to develop space intelligence products and increase the efficiency of the LeoLabs radar network using AI and ML techniques

• Develop, train, and deploy machine learning models in a production environment

• Conduct data analysis, statistical modeling, and algorithm development

• Utilize cloud computing platforms (AWS/GCP/Azure) for deploying and scaling applications

• B.S. or M.S. in Computer Science, Physics, Economics, Statistics, Aerospace Engineering, or a related quantitative field

• At least 3 years of experience in machine learning or a related role, demonstrating a track record of applying ML solutions to practical problems

• Proficiency in Python and familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch)

• Strong problem-solving and analytical skills, excellent communication, and teamwork abilities

• Ability to work in a fast-paced, evolving environment

• Familiarity with cloud environments like AWS/GCP/Azure and data pipeline technologies

• Curiosity about astrodynamics

• Ph.D. or similar rigorous research experience

• Experience in deploying ML models in cloud environments using Kubernetes, Docker containers

• Experience with satellite data, radar signal processing, or orbital mechanics

Short-Term Milestones

• Complete our onboarding program to understand LeoLabs' mission, technology, and team dynamics

• Begin collaboration with the Space Domain Awareness team to understand current machine learning projects and data processing pipelines

• Take ownership of specific machine learning projects, contributing to the development and improvement of models

• Engage with cross-functional teams to integrate machine learning solutions into our service offerings

• Have significantly contributed to the enhancement of our LEO mapping and DA services through advanced machine learning models

• Begin leading new initiatives aimed at leveraging machine learning for future service expansions

• Have established yourself as a key contributor to the machine learning and R&D efforts at LeoLabs

• Drive innovation in our approach to securing and understanding LEO, presenting findings and advancements at industry conferences or in publications

Salary offers are based on a combination of factors, including, but not limited to, experience, skills, and location. The compensation range for this position $165,000 - 180,000

To conform to U.S. Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be eligible to obtain the required authorizations from the U.S. Department of State. More information regarding ITAR can be found at DDTC’s website here.