Lead Real-Time Perception & Control Engineer

Devyantram; Ashburn (VA); On-site; USD 155,000 - 175,000; Full time

Job summary

Devyantram in Ashburn, VA is seeking a senior engineering lead to design the real-time sensing-to-action layer of our robots. You will bridge perception, planning, control, and learning, shaping how the CEREBRION bionic brain interacts with sensors and actuators in real-time, from single embodiments to swarm-scale systems.

The role combines unconventional cognitive substrates with deterministic control, requiring deep expertise in robotics, controls, and real-time software.

Qualifications

• MS or PhD in Robotics, Controls, EE, CS, or related field.
• 8+ years in robotics, controls, or real-time autonomy, including deployment on physical systems.
• Strong foundation in control theory, robot dynamics and kinematics, and real-time systems.
• Excellent Python and C++.

Responsibilities

• Architect end-to-end perception to control pipelines.
• Lead sensor fusion across vision, depth, tactile, and proprioceptive modalities.
• Develop closed-loop control for manipulation, locomotion, and balance.
• Apply both learning-based and classical control methods (MPC, whole-body control, RL hybrids).
• Own the ROS2-based control stack and simulation-to-real transfer.
• Integrate outputs from the cognitive compute layer into deterministic real-time control loops, and own latency and uncertainty across that boundary.
• Design multi-robot and swarm coordination: distributed estimation and control, multi-agent planning, consensus, and formation.
• Drive performance benchmarking in simulation and on hardware.
• Generate core controls and autonomy IP in coordination with patent counsel.

Skills

Python

C++

Control theory

Real-time systems

Education

MS/PhD in Robotics, Controls, EE, CS

Tools

ROS 2

Gazebo

MuJoCo

Isaac Sim

Job description

Devyantram in Ashburn, VA is seeking a senior engineering lead to design the real-time sensing-to-action layer of our robots. You will bridge perception, planning, control, and learning, shaping how the CEREBRION bionic brain interacts with sensors and actuators in real-time, from single embodiments to swarm-scale systems.

The role combines unconventional cognitive substrates with deterministic control, requiring deep expertise in robotics, controls, and real-time software.