AI Engineer – Physical AI & Swarm UAVs

Key Responsibilities

RL Model Training & Optimization:

Develop, train, and evaluate single-agent and multi-agent reinforcement learning algorithms for swarm UAV navigation, control, and mission strategy.

Simulation to Real (Sim2Real):

Utilize robotics simulators to simulate complex environments, refine reward functions, and execute Sim2Real transfer for physical UAV deployment.

Field Testing & Data Collection:

Participate in field test trials at outdoor testing sites (located on the outskirts of Bengaluru) to validate algorithm performance, troubleshoot edge cases, and collect flight data.

Algorithm Pipeline Integration:

Collaborate with software and control systems teams to translate trained PyTorch/TensorFlow models into deployable C++/Python packages for flight hardware.

Must-Haves (Basic Qualifications)

Education

Degree (B.E./B.Tech/M.E./M.Tech) in

an Engineering discipline

(Computer Science, Aerospace, Mechanical, ECE, Robotics, Mechatronics, etc.).

Core Competencies:

Strong, demonstrable capabilities in three foundational pillars:

Mathematics:

Linear Algebra, Vector Calculus, Probability & Statistics, and Optimization principles.

Computer Science:

Data structures, algorithms, modular code design, and object-oriented programming in

Python

.

Artificial Intelligence:

Fundamental understanding of Machine Learning and Reinforcement Learning concepts (e.g., Markov Decision Processes, Policy Gradients, Q-Learning).

Demonstrated Interest in Physical AI:

Strong passion for applying AI to real-world physical platforms (drones, robotics, or autonomous vehicles).

Academic projects, capstone work, or personal side projects are a big plus.

Travel & Testing Willingness:

Willingness to travel regularly to field testing grounds

located at the outskirts of Bengaluru for real-world UAV flight trials.

Work Preference:

Ability to thrive in a

hybrid work environment

(mix of remote/office software development and hands-on outdoor field testing).

Good-to-Haves (Nice-to-Haves)

Robotics Simulators:

Hands-on experience with robotics or physics simulation environments (e.g., Gazebo, AirSim, NVIDIA Isaac Gym, PyBullet, Webots).

Swarm & Multi-Agent AI:

Exposure to Multi-Agent Reinforcement Learning (MARL) algorithms (e.g., MAPPO, MADDPG) or decentralized consensus algorithms.

Robotics Frameworks:

Familiarity with

ROS / ROS2

architecture and node communication.

UAV Hardware & Flight Controllers:

Experience with open-source flight stacks like

PX4

or

ArduPilot

, or basic hands-on experience assembling/debugging UAV hardware.

Systems Programming:

Proficiency in

C++

in addition to Python for real-time edge execution.

What We Offer

Work at the Cutting Edge:

Direct exposure to bleeding-edge Physical AI technologies and multi-drone autonomy.

Real-World Impact:

Watch your algorithms control hardware in real-world field environments rather than staying confined to benchmark datasets.

Mentorship & Growth:

Collaborate closely with senior AI researchers and robotics hardware engineers in an environment built for fast learning and innovation.