3 months ago
Base Salary
$162k - $175k/yr
Responsibilities
- Develop solutions to complex perception problems using deep learning.
- Build, train, evaluate, and deploy state-of-the-art machine learning models in production at scale.
- Communicate ideas verbally and in writing through clear design and project-plan documents.
- Contribute to team roadmap and planning.
- Collaborate with engineering, product, and cross-functional teams to deliver end-to-end customer solutions.
- Work with the platform team on MLOps infrastructure to improve scale and reliability.
Requirements
- 1–2 years of experience building and deploying machine learning models for perception in production settings.
- Hands-on experience designing, training, and evaluating machine learning models for perception.
- Ability to independently build ML pipelines and deploy models to AWS, GCP, or Azure.
- Familiarity with MLOps practices including experiment tracking, model versioning, and automated workflows.
- Hands-on experience with PyTorch and Python.
- Bachelor’s or Master’s degree in Computer Science or a related field.
- Strong communication, self-starting ability, collaboration skills, and adaptability in a fast-paced startup environment.
- Experience with perception problems at self-driving car companies is preferred.
- Familiarity or growing expertise in 3D vision models, video or temporal behavior models, vision-language model fine-tuning, perception foundation models, or the NVIDIA edge device stack and CUDA optimization is valued.
Benefits
- Hybrid schedule based in San Francisco with at least 3 days in-office per week.
About Hayden AI
Hayden AI builds a vision AI platform with vehicle-mounted cameras and analytics that detect road and curb violations and measure transit performance for cities and transit agencies. The company sells hardware plus cloud software and compliance services through multi-year contracts to municipal and transportation customers. Privately held and headquartered in San Francisco, it focuses on North American smart-city deployments such as bus-lane enforcement, curb management, and school-zone safety.