Software Engineer, Deep Learning Acceleration

Who We Are

Aurora’s mission is to deliver the benefits of self‑driving technology safely, quickly, and broadly. The Aurora Driver will create a new era in mobility and logistics, bringing a safer, more efficient, and more accessible future to everyone. At Aurora you will tackle massively complex problems alongside passionate, intelligent individuals, growing as an expert while expanding your knowledge.

• Conduct performance analysis and optimization of Deep Learning networks running on the Autonomous Vehicle (AV).
• Optimize software architecture, system performance, and latency for deep learning applications.
• Work on deployment of deep learning models on the AV and training on large‑scale data centers.
• Troubleshoot performance issues using profiling and roofline model techniques.
• Collaborate with cross‑functional teams to enhance the efficiency of self‑driving technology.

• Minimum 5+ years of professional experience in software engineering.
• BS, MS, or PhD in Computer Science or a related field.
• Strong programming skills in CUDA, C++ and Python.
• Extensive experience in high‑performance computing and parallel programming, specializing in optimizing workloads to reduce GPU memory usage, minimize latency, and/or maximize throughput.
• Proficiency in leveraging performance analysis tools such as NVIDIA Nsight Systems, Nsight Compute and applying techniques like the roofline model for performance optimization.
• Hands‑on experience in optimizing DL/ML workloads at the framework level using at least one deep learning framework (e.g., PyTorch, Tensor Flow), ensuring efficient and scalable model deployment.
• Strong understanding of the fundamentals of computer vision and transformer‑based deep learning architectures, with proficiency in foundational neural network building blocks.
• Strong analytical skills for diagnosing and troubleshooting performance bottlenecks in complex systems.
• Demonstrated ability to quickly learn and adapt to emerging technologies and tools in a fast‑paced environment.
• Experience working on large code bases in a fast‑growing environment.
• Strong communication skills, enabling effective teamwork across multidisciplinary teams.
• Comfortable working in Linux/Unix environments.

• Hands‑on experience in motion planning or related fields such as robotics, autonomous systems, systems software, or computer vision.
• Experience with TensorRT, OpenAI Triton, Mojo and other inference acceleration tools.

The base salary range for this position is $189,000 - $274,000. Aurora’s pay ranges are determined by role, level, and location. Within the range, the successful candidate’s starting base pay will be determined based on factors including job‑related skills, experience, qualifications, relevant education or training, and market conditions. These ranges may be modified in the future. The successful candidate will also be eligible for an annual bonus, equity compensation, and benefits.

At Aurora, we bring together extraordinarily talented and experienced people united by the strength of our values. We operate with integrity, set outrageous goals, and build a culture where we win together — all without any jerks. We believe in‑person work increases collaboration, empathy and our ability to lead effectively. As a result, we operate in a hybrid work environment where Aurorans are in office at least 3 days per week.

Our Careers page provides insight into what it is like to work at Aurora, and you can find all the latest updates in our Newsroom.

At the core of everything we do is our commitment to safety. Building best‑in‑class self‑driving technology will take time, and we believe that each employee at Aurora has a role in contributing to safety, every step of the way. Aurora expects commitment to our safety policies from every employee, and seeks candidates who take an active responsibility, can contribute to building an atmosphere of trust, and invest in the organization’s long‑term success by prioritizing working safely, no matter what.

Aurora considers candidates without regard to their…