Sr. Robotics Software Engineer, Controls
Aureliussystems
| Company | Aureliussystems |
| Category | Engineering |
| Location | San Francisco |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Senior |
| Salary | USD 140k–180k |
| Posted | 30 Jun 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (ashby) |
Description
Who We Are:
Aurelius Systems is a VC backed defense tech startup building autonomous, edge deployed directed energy systems for counter-UAS. We build laser weapons to shoot down drones.
We're a small team of engineers, former US military operators, and subject matter experts scaling America's directed energy dominance. The first cost effective, reliable and robust laser weapon system.
Our namesake isn't an accident. Marcus Aurelius wrote about doing the work in front of you, every day, without excuses. Henry Ford didn't wait for permission to reinvent manufacturing.
That's how we operate — small team, unreasonable output, no hiding behind the unachievable.
In addition to our San Francisco lab, we’ve opened a Detroit manufacturing hub and we field test weekly on our own 400-acre private range.
The Role & Your Impact:
You'll own the precision motion stack, full stop.
The critical gap we're hiring for is a hands-on robotics controls engineer who can iterate fast on real hardware. Someone who can take a model, get it on the hardware, characterize what's actually happening, and tighten the loop. You think in Bode plots and step responses.
This is a senior IC role on the controls side. You'll own the architecture and tuning of the precision pointing and tracking control loops, the state estimation behind them, and the closed loop integration with the tracker.
What You'll Own:
- Architecture and tuning of the precision pointing and tracking control loops
- Closed loop tracking from sensor input through actuator command at hundreds of Hz
- State estimation across IMU, encoder, and vision based feedback (Kalman filters, complementary filters, sensor fusion)
- System identification and modeling of the gimbal, mount, and actuator dynamics
- Actuator tuning, gain scheduling, and compensation for nonlinearities (backlash, friction, saturation)
- Resonance characterization, notch filtering, and structural compensation
- Simulation infrastructure for controls development
- Hardware-in-the-loop test infrastructure for controls validation
- Field characterization of the system against real targets at the range
- Author controls documentation and tuning standards
What We're Looking For:
- 4+ years of experience in robotics
- Strong knowledge of fundamentals: classical controls, state space, LQR, MPC
- Proven simulation experience and a track record of managing the sim-to-real gap on actual hardware
- Modern C++ for real time control implementation
- Python or MATLAB/Simulink for modeling, system ID, and analysis
- State estimation in practice (Kalman, EKF, UKF, complementary filters)
- System identification and frequency domain tuning
- Direct experience with motion control hardware — servo drives, brushless motors, encoders, IMUs
- Comfortable working at the seam between mechanical, electrical, and software
- Understanding of latency, jitter, and how they kill control loop performance
Not a fit if: Your controls experience is purely simulation, your real world tuning amounts to changing PID gains in a UI, or you've never had to characterize a resonance, model an actuator, or fight a sensor that lies to you. We need someone who's tuned hardware that fought back.
Nice-to-Haves:
- Advanced MPC — nonlinear MPC, robust MPC, or real-time NMPC on resource constrained hardware
- Gimbal, beam director, or precision pointing system experience
- Aerospace, defense, or directed energy controls background
- Adaptive or robust control implementation in production
- Vision-in-the-loop control (visual servoing, image-based tracking)
- Hardware-in-the-loop simulation experience
- High bandwidth sensor pipelines (cameras, radar, IMUs at high rate)
- Prior work on a system that had to hit a moving target
Education:
MS in Mechanical Engineering, Aerospace Engineering, Electrical Engineering, Robotics, or related field is the sweet spot. BS with strong hands-on robotics experience or Ph