About Kessari:

We are building autonomy systems that operate in GNSS-denied and degraded environments. This is not a research role. This is a real-world deployment on physical platforms.

We are specifically looking for engineers who think in terms of uncertainty, not clean inputs.

What this engineer must already think like:

Vision is probabilistic, not deterministic
Tracking is more important than detection (maintaining belief over time)
Comfortable working with degraded inputs: low light, motion blur, packet loss, partial frames
Design systems that continue operating when vision fails (fallback logic, sensor fusion)

Hard requirements (non-negotiable):

Strong experience with multi-object tracking:

Kalman filters, particle filters, JPDA, SORT/DeepSORT or similar

Sensor fusion experience:

At minimum camera + IMU
Bonus if experience with GNSS-denied navigation

Real-time systems experience:

Edge compute, latency constraints, performance tradeoffs

Has deployed on real physical systems:

UAVs, robotics, automotive, or defence systems
Strong C++ (required), Python acceptable alongside

Nice to have (not required):

SLAM / VIO (visual-inertial odometry)
Tracking under occlusion and re-identification
Experience in degraded or contested environments (e.g. EW, poor comms)
Familiarity with PX4, ArduPilot, ROS

What we do NOT want:

Pure machine learning engineers with no deployed systems
Candidates focused only on object detection (YOLO-style pipelines)
Academic researchers without real-world deployment experience

What they will be doing:

Building and improving tracking systems that maintain target identity over time
Designing sensor fusion pipelines robust to degraded inputs
Working on real-time perception systems running on edge hardware
Ensuring system reliability when inputs are unreliable or partially missing

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Perception/Autonomy Engineer (Degraded & Denied Environments)