Harmattan AI is a next-generation defense prime building autonomous and scalable defense systems. Following the close of a $200M Series B, valuing the company at $1.4 billion, we are expanding our teams and capabilities to deliver mission-critical systems to allied forces.
Our work is guided by clear values: building technologies with real-world impact, pursuing excellence in everything we do, setting ambitious goals, and taking on the hardest technical challenges. We operate in a demanding environment where rigor, ownership, and execution are expected.
About the Role
We are developing advanced autonomous systems that rely on robust state estimation and sensor fusion to operate in complex, dynamic environments. Our platforms integrate multiple sensors (e.g., IMU, GNSS, vision, barometer, magnetometer) and require accurate, real-time estimation of system states (position, velocity, attitude, etc).
Classical approaches such as Kalman filtering are powerful but rely on modeling assumptions that often break down in real-world conditions. To push performance beyond these limits, we are exploring hybrid approaches that combine model-based estimation and control with modern machine learning techniques.
Your mission
The goal of this internship is to explore and apply machine learning-based sensor fusion and state estimation methods to improve performance in dynamic environments.
Responsibilities
Literature review: Conduct a comprehensive review of existing ML methods for state estimation and sensor fusion.
Algorithm Implementation: Develop and implement various algorithms based on the literature review and project requirements using simulated and real-world flight data.
Performance evaluation: Assess and compare the performance and computational overhead of the developed algorithms with classical baselines.
Documentation: Document all work performed, including methodologies, results, and conclusions.
Flight Tests Participation: Actively participate in flight test sessions to gather real-world data and validate the effectiveness of the developed algorithms in operational conditions. Contribute to real-time deployment.
Candidates Requirements
Educational Background: A strong academic record in applied Mathematics (especially machine learning). Knowledge of sensor fusion/state estimation is a strong plus.
Technical Skills: Strong understanding of ML fundamentals. Experience with State Estimation, drones, or Control Theory is a major plus.
Mindset: You are curious to learn, autonomous and able to take initiative.
We look forward to hearing how you can help shape the future of autonomous defense systems at Harmattan AI.
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