Machine Learning Engineer

Los Gatos, CA /
E-Space – Satellite Engineering /
/ On-site
Ready to make connectivity from space universally accessible, secure and actionable? Then you’ve come to the right place!
At E-Space, we’re focused on bridging Earth and space with the world’s most sustainable low Earth orbit (LEO) satellite network. We’re a team of bold thinkers, ambitious leaders and dynamic doers—and we’re disrupting NewSpace by fundamentally changing the design of legacy LEO space systems to deliver entirely new satellite capabilities at a fraction of the cost. ​
We’re intentional, we’re unapologetically curious and we’re 100% committed—to saving space, to protecting our planet and to turning connectivity into actionable intelligence.

What is the role?
We are looking for people who like to solve complex problems and thrive in a fast-paced environment. The best candidates are passionate about advancing the use of space for humanity and solving some of earth’s biggest challenges. 
As a Machine Learning Engineer, you will be a critical member of the Autonomous Flight Systems and Simulations Team continuously improving our flight systems and simulations. 
This position will report to the Head of Simulations and will work closely with the engineering and regulatory team.  This position will be based in Los Gatos, CA. 

What you will do

    • Seamlessly integrate logged data replay and simulated agents  
    • Develop novel machine learning methods and algorithms that solve complex problems, including but not limited to, path planning, time-series, depth estimation, etc. 
    • Filter and mine large scale simulated data to detect the most interesting issues  
    • Accelerate onboard software development with improved tools and visualization 
    • Utilize your data modeling expertise

Why you're the right candidate

    • You have at least 2 years' experience in a professional setting or equivalent experience working as a Machine Learning engineer   
    • You have a Bachelor's or Master's degree in Computer Science or a similar discipline or equivalent industry experience 
    • You have experience developing novel machine learning methods and algorithms that solve complex problems, including but not limited to, path planning, time-series, depth estimation, etc. 
    • You have proven experience navigating a large code base  
    • You have a passion for working with satellites and communications  
    • You have experience with scripting languages such as Python (must) or MATLAB (preferred)  
    • You have demonstrated effective communication skills and the ability to articulate ideas and timelines to cross-functional groups  

Additional experience we'd like to see

    • Significant statistical experience in data-intensive on-line systems  
    • Experience building predictive models and machine learning principles (clustering analysis, failure prediction, anomaly detection)  
    • Demonstrated ability to own projects from start to completion  
    • Strong attention to detail  

Please note that this role may require call for extended work hours during busy periods.

The estimated base salary range for this role is $100,000-180,000 per year. The estimated range is meant to reflect an anticipated salary range for the position in question, which is based on market data and other factors, all of which are subject to change. Individual pay is based on location, skills and expertise, depth of relevant experience, and other relevant factors. For questions about this, please speak to the recruiter if you decide to apply for the role and are selected for an interview.


Why E-Space is right for you
We want you to make the most of your journey at E-Space. That’s why we support and invest in the physical, emotional and financial well-being of our team members and their families. Some of what you can expect when working at E-Space:
·        An opportunity to really make a difference
·        Sustainability at our core
·        Fair and honest workplace
·        Innovative thinking is encouraged
·        Competitive salaries
·        Continuous learning and development
·        Health and wellness care options
·        Financial solutions for the future
·        Optional legal services
·        Paid holidays
·        Paid time off