Senior Software Engineer – ADAS

Requirements:

• 4–8 years of professional software engineering experience, ideally in ADAS, automotive, robotics, or real-time systems.
• Master’s or PhD degree in Computer Science or in Machine Learning.
• Strong modern C++ (C++14/17 or later): templates, RAII, smart pointers, STL, and experience building large codebases.
• Solid Python skills for tooling, training scripts, and glue code between data pipelines and C++ components.
• Hands-on experience training and using deep learning models (PyTorch or TensorFlow): designing experiments, tuning hyperparameters, working with large datasets, and debugging model behavior.
• Experience developing on Linux: build systems (CMake), debugging (gdb, sanitizers), profiling, and git-based workflows in a CI/CD environment.
• Familiarity with GPU programming and optimization (CUDA, TensorRT, cuDNN), Computer vision/perception, Robotics or autonomous systems.

What you'll be doing:

• Design, implement, and maintain C++ ADAS functions for perception, prediction, and planning.
• Integrate deep learning models into C++ pipelines and deploy them for real-time inference on NVIDIA GPUs.
• Work with multi-sensor data and implement sensor fusion, tracking, and decision-making logic.
• Build and extend testable, modular libraries and components, including interfaces to models, sensor drivers, and vehicle control.
• Profile, debug, and optimize C++ and CUDA code to meet strict latency and throughput targets.
• Contribute to tooling around data quality, automated evaluation, and regression tests for ADAS functions.
• Collaborate closely with ML researchers, systems engineers, and automotive partners to turn prototype algorithms into production-ready C++ implementations.

Nice to haves:

• Direct experience implementing ADAS functions in C++, such as lane keeping, adaptive cruise control, automatic emergency braking.
• Experience with camera calibration, sensor fusion, or multi-camera perception systems.
• Knowledge of model optimization and deployment: quantization, TensorRT-LLM, ONNX Runtime, or similar frameworks.
• Background in training infrastructure, understanding of software quality practices for safety-critical systems, and open-source contributions or published work in AI, robotics, or GPU computing.

Perks and benefits:

• Work on challenging, real-world ADAS and autonomous driving problems.
• 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.