Applied Intuition is looking to solve the problem of accelerating the global adoption of safe, AI-driven machines by delivering the Vehicle OS, Self-Driving System, and toolchain to help customers build intelligent vehicles and shorten time to market.
Requirements
- Experience with building software components to address production, full-stack machine learning challenges
- Opinions about building a company-wide platform for ML training, evaluation, and deployment
- Knowledge of the open source landscape with judgment on when to choose open source versus build in-house
- Excellent analytical and problem-solving skills
- Experience with developing, running, and managing orchestration systems like Airflow and Flyte
- Experience with ML modeling frameworks (PyTorch, Tensorflow, etc.), and model serving platforms (TorchServe, TensorFlow Serving, NVIDIA Triton inference server, etc.)
Responsibilities
- Design and implement distributed cloud GPU training approaches for deep learning model training and evaluation
- Build end-to-end machine learning pipelines and integrate them into core product workflows
- Encourage change, especially in support of ML engineering best practices, and maintain a high standard of excellence
- Collaborate with engineers across the entire company to solve complex data problems at scale
Other
- In-office work 5 days a week with occasional remote work
- Bachelor's, Master's, or Ph.D. degree (not explicitly mentioned but implied)
- Ability to work in a team and collaborate with external and internal users
- Must be eligible to work in the location listed
- Equal opportunity employer and federal contractor or subcontractor, abiding by regulations prohibiting discrimination
