GNC Engineer - Navigation Sensors and Algorithms
K2 is building the largest and highest-power satellites ever flown, unlocking performance levels previously out of reach across every orbit. Backed by over $1 billion in total funding from leading investors including Altimeter Capital, ICONIQ, Kleiner Perkins, Lightspeed Venture Partners, Redpoint Ventures, and T. Rowe Price — and with over $1 billion in signed contracts across commercial and US government customers, we're mass-producing the highest-power satellite platforms ever built for missions from LEO to deep space.
The rise of heavy-lift launch vehicles is shifting the industry from an era of mass constraint to one of mass abundance, and we believe this new era demands a fundamentally different class of spacecraft. Engineered to survive the harshest radiation environments and to fully capitalize on today's and tomorrow's massive rockets, K2 satellites deliver unmatched capability at constellation scale and across multiple orbits.
With multiple launches in 2027 and plans to scale to 100 satellites a year, we're Building Bigger — helping develop the solar system and build toward a Kardashev Type II (K2) civilization. If you are a motivated individual who thrives in a fast-paced environment and you're excited about contributing to the success of a high-growth Series D-funded company, we'd love for you to apply.
The Role
You will own the navigation and sensing chain that tells K2 spacecraft where they are, how they are oriented, from the physical sensor hardware all the way to the estimation algorithms and flight software executing those algorithms. This is a blend of a hands-on hardware and software role, requiring you to be the GNC point of contact with Avionics, flight software, and Test teams. In the first 6 months, you will help to design and characterize the software driver for the GNSS receivers, standing up develop testing to validate GNSS performance and link budgets as a function of altitude, and feeding these learnings back into both hardware and on board filter design. In the first year you will support multiple K2 launches of the stacked configuration, supporting operations as the GNC point of contact.
Responsibilities
- Design, implement, and verify onboard estimation algorithms for orbit and attitude determination (EKF/MEKF/UKF, batch estimators, sensor fusion)
- Develop high-altitude GNSS navigation capability for MEO and GEO, including sidelobe and weak-signal tracking, receiver selection, and onboard orbit filtering
- Improve measurement and system timing accuracy from sensor output to GNC ingestion, ensuring low-latency and low-jitter navigation
- Develop and run GNC sensor calibration on the ground and on orbit.
- Select, specify, and integrate navigation sensors (GNSS receivers, IMUs, magnetometers, star trackers) in fault tolerant architectures to support robust next generation capabilities
- Build sensor models and estimation components in our simulation framework, and bring algorithms from prototype into production flight software
- Support on-orbit commissioning and navigation performance analysis during mission operations
- Defining pass/fail performance criteria for GNC hardware during acceptance testing and supporting investigation of out-of-spec hardware
- Shape the future of the GNC team by recruiting, interviewing, and mentoring engineers
Qualifications
- Master’s degree or higher in aerospace, electrical, or mechanical engineering, physics, or a related field
- 2+ years of experience in navigation, estimation, or sensor engineering for spacecraft.
- Strong grasp of estimation theory, with hands-on experience implementing filters for real time systems
- Hands-on experience with navigation hardware in the lab
- Experience with Python, Julia, C/C++, or Rust for algorithm development, implementation, hardware test, and simulation
Nice to Have
- Experience with early-stage spacecraft conceptual design, i.e. “clean sheet” design work
- Previous experience with high altitude/cis-lunar GNSS architectures
- Knowledge of GNSS link budgets and antenna pattern modeling