Description :

Lead Founding Engineer AI, Autonomous Systems, Aeromechanics & Infrastructure Intelligence

About AB Labs :

AB Labs is a stealth-mode deep-tech startup initiative currently being developed under the DivyaSree umbrella, focused on building autonomous intelligence systems across advanced sensing, robotics, edge AI, and infrastructure-scale operational environments.

The lab is designed to translate research-grade systems into deployable, mission-ready platforms across sectors such as autonomous systems, defense-grade systems, space systems, industrial intelligence, and future infrastructure operations.

The Role :

You will lead the software, autonomy, mathematical modeling, and systems architecture side of our platforms, translating distributed sensing, telemetry streams, environmental inputs, operational signals, and vehicle dynamics into usable intelligence for real-world autonomous and operational systems.

This is a founding leadership role in a high-pressure, fast-paced environment. You will be responsible for moving from architecture notes, simulation environments, white-paper research, and mission concepts into deployment-ready autonomous and intelligence systems at speed.

As a founding technical leader, you are an owner, not just an employee. You will help define the technical core, autonomy stack, sensing architecture, control systems framework, and intelligence roadmap of the platform.

You will work across AI, robotics, aeromechanics, sensor fusion, telemetry systems, distributed compute, mechatronics, embedded systems, infrastructure intelligence, and operational workflows to build scalable autonomous systems capable of operating across infrastructure-scale, robotic, and mission-critical environments.

You are expected to operate comfortably across the full stack of autonomy from perception, mapping, and localization to control systems, optimization, deployment, and field validation while helping shape the long-term engineering direction of the lab.

Core Responsibilities :

1. Autonomous Systems Architecture : Define the architecture of autonomous intelligence systems operating across drones, robotic platforms, distributed sensing, telemetry, edge compute, and operational environments.

2. Algorithm & Intelligence Strategy : Lead development of AI, signal processing, sensor fusion, state estimation, mapping, localization, anomaly detection, forecasting, and operational intelligence systems.

3. Perception, Detection & Tracking Systems : Develop scalable perception pipelines for object detection, tracking, surveillance, inspection, mapping, environment understanding, and mission-relevant event detection.

4. Control Systems & Vehicle Dynamics : Design and implement control systems, trajectory planning, optimization routines, flight dynamics models, stability logic, and closed-loop autonomy for aerial and robotic platforms.

5. Sensor Fusion & Precision Engineering : Establish robust standards for sensor synchronization, data integrity, calibration, uncertainty handling, multi-modal fusion, telemetry validation, and field performance evaluation.

6. Platform, Edge & Hardware Optimization : Guide deployment of intelligence systems across embedded, edge, cloud, and distributed compute environments while optimizing for latency, reliability, power, memory, throughput, and operational scale.

7. Mechatronics & Embedded Integration : Integrate software intelligence with microcontrollers, sensors, actuators, embedded platforms, hardware accelerators, robotics hardware, and real-world mechanical/electrical systems.

8. System Integration & Mission Alignment : Partner with AI, robotics, sensing, infrastructure, platform, and operations teams to ensure autonomy logic aligns with real-world system behavior, mission goals, safety constraints, and deployment conditions.

9. GNSS-Denied & Resilient Systems : Develop resilient autonomy and navigation approaches for degraded, uncertain, or GNSS-denied operational environments.

10. Noise, Uncertainty & Integrity-Aware Systems : Build uncertainty-aware sensing, state estimation, and operational intelligence systems under noisy or degraded sensing conditions.

11. Simulation, Digital Twin & Validation : Work across simulation, digital twins, SITL/HITL validation, telemetry analysis, and deployment-readiness evaluation.

12. Interdisciplinary Collaboration : Collaborate across engineering, research, and academic ecosystems including IISc, IITs, BITS, and international research partners.

13. Execution Leadership : Translate research concepts, simulations, and architecture frameworks into engineering workstreams, milestones, ownership structures, prototypes, and deployment-ready systems.

14. The Founder Mindset / Ownership : Take full accountability for the autonomy and intelligence roadmap, from initial architecture and simulation to field-ready operational capability.

15. Ambiguity : Operate effectively in undefined environments, create structure where none exists, and turn unclear technical problems into executable engineering paths.

16. Speed & Rigor : Balance rapid execution with scientific depth, systems validation, field testing discipline, and long-term platform quality.

17. Scrappiness : Be comfortable operating simultaneously as architect, reviewer, mentor, debugger, field engineer, systems integrator, and hands-on builder when required.

Key Tools & Technologies :

C++, Python, MATLAB/Simulink, ROS/ROS2, PX4, MAVLink, TensorFlow/PyTorch, OpenCV, embedded Linux systems, microcontrollers, signal processing frameworks, simulation environments, and distributed telemetry systems.

Strong familiarity with :

- Robotics and autonomous systems

- Control systems and optimization

- Sensor fusion and operational perception

- Hardware/software co-design

- Embedded and edge AI deployment

- Quantization and inference optimization techniques

- CPU/GPU acceleration and hardware-aware optimization

- Cloud-native and distributed compute systems

- Streaming protocols and operational data pipelines

Experience with platforms and ecosystems such as :

- NVIDIA / Intel / Qualcomm AI stacks

- Kubernetes and distributed infrastructure

- AutoSAR and embedded automotive/robotics systems

- Hardware accelerators and deployment-focused AI workflows

Preferred Background :

- Masters or PhD preferred in Robotics, Aerospace Engineering, AI/ML, Computer Science, Mechatronics, Systems Engineering, Embedded Systems, Cyber-Physical Systems, Electronics, Control Systems, Autonomous Systems, or related technical domains from Tier 1 or strong Tier 2 engineering institutions.

- Exceptional candidates with strong hands-on project experience, research contributions, startup exposure, open-source work, or deep systems-level engineering capability are strongly encouraged, irrespective of traditional academic pedigree.

Experience :

- 5-8 years of relevant experience across autonomous systems, robotics, UAVs, aeromechanics, AI/ML systems, embedded and edge AI, sensor fusion, distributed systems, control systems, interdisciplinary engineering, operational intelligence, deep-tech startups, or mission-critical engineering environments.

- Strong preference for candidates who have worked across the full stack from algorithms and simulations to deployment, optimization, embedded integration, interdisciplinary system integration, and real-world operational validation.

Primary Output :

- Scalable autonomous intelligence architectures, mission-ready robotics and UAV systems, distributed sensing platforms, engineering roadmaps, cross-domain system integration frameworks, and deployable mission-critical operational systems.

- You will help shape the long-term technical direction of the lab while driving architecture decisions, engineering decomposition, deployment strategy, technical mentorship, and interdisciplinary collaboration across industry and leading research ecosystems including IISc, IITs, BITS, and international academic partners.