Autonomous Vehicle Control Engineer

About the Job

A Chennai based Zoho group of company is seeking an experienced engineer to design, train, and deploy reinforcement learning (RL) policies for an Ackermann-steered robotic vehicle. This role involves full stack ownership from data pipelines and simulation to on-vehicle inference spanning perception, planning / control, and MLOps.

You should be skilled in PyTorch (primary), with production experience in TensorFlow / Keras, and possess strong data engineering capabilities.

What You’ll Do

Reinforcement Learning

• Design and implement RL / IL algorithms for lateral and longitudinal control (e.G., SAC, TD3, PPO, IQL / CQL, BC / DAgger).
• Build reward functions and constraints that respect Ackermann kinematics, curvature limits, tire slip, and comfort.
• Contribute to safe and constrained RL frameworks with uncertainty estimation and fallback control strategies (PID / MPC).

Perception and Vision

Planning and Control Integration

Data engineering and MLOps

Simulation and testing

On‑vehicle deployment

Collaboration

mentor junior engineers.

Minimum Qualifications

■ RL / IL (value / policy gradients, offline RL, dataset curation, covariate shift handling).

■ Control and estimation (Ackermann kinematics / dynamics, PID / MPC, EKF / UKF).

BEV / occupancy).

Preferred Qualifications

Key Outcomes (First 90–180 Days)

Establish a data pipeline from vehicle logs to curated train / eval datasets with automated quality checks.

Stack You’ll Use

ML : PyTorch (primary), TensorFlow / Keras, ONNX / TensorRT, CUDA Vision : OpenCV, torchvision, Detectron / YOLO, BEV / occupancy frameworks Robotics : ROS2, nav2, C17 / 20 Data & MLOps : Python, Pandas / Numpy, Arrow / Parquet, DVC / MLflow / W&B, Docker, Git, CI Simulation : CARLA, Isaac Sim, Gazebo, AirSim