Applied Intuition
Software Engineer - E2E Autonomy
About this role
Applied Intuition is hiring a Software Engineer to build ML infrastructure and tools supporting end-to-end autonomous vehicle research and development. You'll collaborate with AI research and engineering teams to optimize large-scale training systems, manage datasets, and address bottlenecks across the autonomy stack.
What you'll do
- Build tools and infrastructure for large-scale E2E autonomy research
- Productionize next-generation self-driving software with AI research and engineering teams
- Identify and resolve bottlenecks in end-to-end training pipelines
- Scale GPU compute, on-road data processing, and evaluation systems
- Design modular software components and abstractions for the ML stack
- Contribute across vehicle, cloud, and infrastructure layers
What they're looking for
- Python and PyTorch
- CUDA
- Bazel and build systems
- Kubernetes and distributed systems
- Software architecture and module design
- ML infrastructure and data systems
- Problem-solving and analytical thinking
- Cloud services and scalable compute
Benefits
- Competitive base salary ($153,000 - $222,000 USD annually)
- Equity in the form of options or restricted stock units
- Comprehensive health, dental, vision, life and disability insurance
- 401k retirement benefits with employer match
- Learning and wellness stipends
- Paid time off with flexible in-office policy
Opens the application — the Jobs AI extension fills it for you. Set up autofill
Opens the official application on the employer’s site. No login required.
Applied Intuition
Applied Intuition builds autonomous vehicle and defense systems software, including motion planning algorithms, simulation infrastructure, and autonomy integration platforms for aerial and ground platforms. The company is hiring for security engineers, robotics/autonomy software engineers, hardware-in-the-loop specialists, and IT operations professionals to support its growing physical AI operations.
- Website
- appliedintuition.com
Likely interview questions
- Walk us through a time you built ML infrastructure or tools that had to scale significantly. What bottlenecks did you encounter and how did you solve them?
- Describe your experience with distributed compute systems like Kubernetes or Spark. How have you debugged performance issues in these environments?