Machine Learning Engineer

Voyager is an innovative space, defense, and national security technology company committed to advancing and delivering transformative, mission-critical solutions. We tackle the most complex challenges to unlock new frontiers for human progress, fortify national security, and protect critical assets to lead in the race for technological and operational superiority from ground to space.

Forge the Future: Join Voyager Technologies

The future belongs to those who build it. At Voyager Technologies, we’re building technologies that protect lives, expand frontiers and prepare us for what’s next. And we’re doing that with people who are wired to solve, build, adapt and lead. These roles are not for the faint of heart.

You’ll help lay the foundation for humanity's future. Join a culture where innovation thrives, curiosity is rewarded, and impact is real. We’re a company of doers, thinkers and builders, united by purpose and grounded in reality.

If you want to put your skills to work where the stakes are real and the mission is bigger than any one person, forge the future with Voyager.

The Company is currently seeking Machine Learning Engineers who can develop cutting edge Deep Learning technologies applied to pressing problems in the domain of Computer Vision, Synthetic Aperture Radar (SAR), and Geospatial Exploitation. You will have considerable responsibility, and your role will have direct impact on relevant issues facing the United States government customers.

This posting stays open continuously. We hire machine learning engineers at several levels, from early career to senior staff, and review applications on a rolling basis. Your level and title are determined during the hiring process, based on your experience and the scope of your past work. See Levels We Hire For below.

This position is based in El Segundo, CA. If your experience aligns with our required qualifications and you are inspired by our mission, we’d like to connect. If you don’t see the right role right now, Join Our Talent Community and stay connected as we continue to build what’s next.

In this role, the essential functions are:

We hire at five levels; each row shows the typical experience and scope.

Title

Typical experience

Scope

Machine Learning Engineer I

0–2 years

Implements models and ML pipelines under guidance. Usually holds a BS or MS and may have internship or research experience.

Machine Learning Engineer II

2–5 years

Independently develops and deploys ML solutions. Can take an ML problem from requirements through implementation, including production deployment, model monitoring, feature engineering and experimentation.

Senior Machine Learning Engineer

5–8 years

Owns complex ML systems and technical projects. Makes architecture decisions, mentors junior engineers, and works closely with product, data and platform teams.

Staff Machine Learning Engineer

8–12+ years

Provides technical leadership across teams and systems, beyond simply owning larger individual projects. Defines architecture and technical direction across multiple teams and influences engineering standards and ML strategy.

Senior Staff Machine Learning Engineer

12–15+ years

Sets organization-wide technical strategy and architecture. Responsible for very large technical problems and/or company-wide ML architecture and strategy.

At every level, you will:

• Research, conceive, design, prototype and/or implement advanced Deep Neural Network Architectures as well as other relevant Machine Learning models.
• Apply deep learning algorithms to train neural networks for object detection and classification.
• Develop domain-aware pre-processing algorithms, and other methods to increase model generalization.
• Apply signal pre-processing techniques on SAR data.
• Create physics simulations of various phenomenologies and develop methods to account for bias between simulated and real-world data.
• Develop appropriate objective functions and sound performance assessment plans.
• Present work at meetings and conferences.
• Develop and deliver production-quality code (mainly Python).

As you move up in level, responsibilities build on those above:

• Machine Learning Engineer II and above: independently take machine learning problems from requirements through implementation, including production deployment, model monitoring, feature engineering and experimentation.
• Senior Machine Learning Engineer and above: own complex machine learning systems and technical projects, make architecture decisions, mentor junior engineers, and work closely with product, data and platform teams.
• Staff Machine Learning Engineer and above: provide technical leadership across teams and systems, define architecture and technical direction across multiple teams, and influence engineering standards and ML strategy.
• Senior Staff Machine Learning Engineer: set organization-wide technical strategy and architecture, and own very large technical problems and/or company-wide ML architecture and strategy.

If your experience aligns with our basic qualifications and you are inspired by our mission, we’d like to connect. If you don’t see the right role right now, Join Our Talent Community and stay connected as we continue to build what’s next.

1. Qualifications build by level: each level includes everything from the level before it, plus the items listed. Years shown are years of applicable experience.

   Machine Learning Engineer I (0–2 years)

   • Bachelor’s degree in engineering or a related field.

   Machine Learning Engineer II (2–5 years): all Machine Learning Engineer I qualifications, plus:

   • Experience independently taking an ML problem from requirements through implementation.
   • Experience with production deployment, model monitoring, feature engineering and experimentation.

   Senior Machine Learning Engineer (5–8 years): all Machine Learning Engineer II qualifications, plus:

   • A record of owning complex ML systems and technical projects.
   • Experience making architecture decisions and mentoring junior engineers.
   • Experience partnering with product, data and platform teams.

   Staff Machine Learning Engineer (8–12+ years): all Senior Machine Learning Engineer qualifications, plus:

   • A record of technical leadership across teams and systems, beyond owning larger individual projects.
   • Experience defining architecture and technical direction across multiple teams.
   • Experience influencing engineering standards and ML strategy.

   Senior Staff Machine Learning Engineer (12–15+ years): all Staff Machine Learning Engineer qualifications, plus:

   • A record of setting organization-wide technical strategy and architecture.
   • Experience owning very large technical problems and/or company-wide ML architecture and strategy.
   • Experience developing or guiding advanced machine learning, signal-processing and exploitation algorithms, and leading technical efforts across organizational and disciplinary boundaries.
   • Ability to evaluate unfamiliar sensors and datasets using first-principles reasoning.
   • Ability to communicate complex technical concepts to expert and non-expert audiences, mentor senior technical staff, and operate effectively with limited direction.