SENIOR MACHINE LEARNING ENGINEER IIRocket Lab's Optical Systems division solves mission-critical space domain and Intelligence, Surveillance, and Reconnaissance (ISR) challenges for Department of Defense (DoD) and Intelligence Community (IC) customers. Our vision is to revolutionize the space-based payload market with innovative and novel designs for space, terrestrial, and airborne environments. Building on more than 20 years of electro-optical and infrared systems innovation, Optical Systems delivers solutions to the warfighter for responsive, scalable sensing solutions across all orbital domains.
As a Senior Machine Learning Engineer II based at our Optical Systems sites in Chantilly, VA, you will have the opportunity to support Tranche 3 of the U.S. Space Development Agency's (SDA) Proliferated Warfighter Space Architecture (PWSA) and beyond by building deep learning neural networks for advanced Electro-Optical (EO/IR) image processing.
WHAT YOU'LL DO- Contribute your experience to developing solutions for real-world problems.
- Design, train, and deploy machine learning models for Optical Systems applications
- Build and maintain ML pipelines for data ingestion, feature engineering, training, and inference.
- Collaborate with other researchers/engineers on artificial intelligence, machine learning, and computer vision.
- Preform rapid prototyping and enhanced development to be integrated into operational systems.
- Perform troubleshooting, bug fixes, and maintenance of existing and new systems.
- Stay current with ML research and evaluate new tools, frameworks, and techniques
WHAT WE'RE LOOKING FOR IN A SENIOR MACHINE LEARNING ENGINEER II:- Bachelor's degree and 8+ years of experience, master's degree and 6+ years of experience, or a Ph.D. in Computer Science, Electrical and Computer Engineering, Mechanical Engineering, Physics, or related field
- Strong background in machine learning including model selection, architecting, training, validation, testing, and deployment
- Experience in building deep learning neural network for computer vision, image processing, or video analysis (e.g., object detection, image segmentation, and tracking)
- Strong math background, particularly linear algebra
- Proficiency in Python
- Experience with deep learning libraries (Keras, TensorFlow, Pytorch, etc)
- Software engineering fundamentals: version control, testing, CI/CD, containerization
- S. citizenship is required, due to program requirements
- Ability to obtain and maintain an U.S. Government Security Clearance
NICE TO HAVE: - Proficiency in C/C++/Rust
- Experience curating quality, real-world datasets for training deep learning models
- TS/SCI security clearance
This position may require prolonged periods of sitting, standing, walking, computer work, and occasional exposure to moderate levels of noise, dust, and fumes in production areas.
The expected salary range for the position is displayed in accordance with the state of Virginia's pay transparency requirements. Final agreed upon compensation is based upon individual qualifications and experience.
Base salary is only one part of Rocket Lab's compensation package for this role. You may be eligible for company stock, or cash incentives, and can purchase discounted stock through Rocket Lab's Employee Stock Purchase Program. Employee benefits also include medical, dental, and vision insurance coverage; 401(k) retirement plan options; paid vacation, holidays, and sick leave; paid parental leave; and other discounts and perks.
Base Pay Range (VA Only)
$153,000-$229,500 USD
WHAT TO EXPECT We're on a mission to unlock the potential of space to improve life on Earth, but that's not an easy task. It takes hard work, determination, relentless innovation, teamwork, grit, and an unwavering commitment to achieving what others often deem impossible. Our people out-think, out-work, and out-pace. We pride ourselves on having each other's backs, checking our egos at the door, and rolling up our sleeves on all tasks big and small. We thrive under pressure, work to tight deadlines, and our focus is always on how we can deliver, rather than dwelling on the challenges that stand in the way.
