Senior Software Engineer – AI and Autonomous Driving

Requirements:

• 4–8 years of professional software engineering experience, ideally in AI, robotics, or automotive domains
• Proficiency in C++ (modern C++14/17 or later) and Python, with demonstrated experience writing clean, maintainable code
• Hands-on experience training deep learning models (PyTorch or TensorFlow), designing experiments, tuning hyperparameters, working with large datasets, and debugging model behavior
• Strong Linux development skills: building, debugging, profiling, version control (git), and working within CI/CD workflows. Familiarity with GPU programming and optimization (CUDA, TensorRT, cuDNN), computer vision and perception (object detection, segmentation, multi-object tracking), robotics or autonomous systems (ROS, ADAS features, simulation environments)

Nice to Haves:

• Experience with camera calibration, sensor fusion, or multi-camera perception systems
• Knowledge of model optimization and deployment: quantization (INT8, FP8, 4-bit), TensorRT-LLM, ONNX Runtime, or similar frameworks
• Background in training infrastructure: distributed training, experiment tracking, dataset versioning, hyperparameter optimization
• Understanding of software quality practices for safety-critical systems (code review, unit testing, static analysis; automotive standards knowledge is a plus)
• Open-source contributions or published work in AI, robotics, or GPU computing

What you'll be doing:

• Design, develop, and maintain C++ and Python software for perception, prediction, and planning in advanced driver-assistance and autonomous driving systems
• Train, fine-tune, and iterate on deep learning models (vision, multimodal, and transformer-based architectures) using large-scale driving datasets, then optimize them for real-time inference on NVIDIA GPUs
• Work with multi-sensor data — cameras, radar, lidar — and contribute to training pipelines, data quality workflows, and automated evaluation infrastructure
• Debug and resolve performance bottlenecks, edge cases, and integration challenges in a complex, safety-critical codebase
• Collaborate with ML researchers, systems engineers, and automotive partners to bring features from research prototypes to production-ready systems

Perks and Benefits:

• Work on challenging, real-world problems where your code directly impacts vehicle safety and performance
• Collaborate with a talented, multidisciplinary team of researchers, engineers, and automotive experts
• Solve hard technical problems at the intersection of deep learning, real-time systems, and production software engineering
• If this opportunity aligns with your background and interests, please apply with your resume and a brief description of relevant projects (links to GitHub, publications, or technical write-ups are welcome)