Design and deploy production machine learning models and AI-powered applications, build intelligent agents and automated workflows, and improve model performance, security, and engineering productivity.
Deep Learning Researcher
📜 Description
- Design and train deep learning models tailored for Mobileye's custom EyeQ chip.
- Tackle end-to-end challenges and deploy real-world solutions.
- Work closely with software and hardware teams to turn research into production-ready systems.
- Contribute to the core neural network architecture powering flagship products.
- Engage in novel architectures and advanced training techniques.
🛠️ Requirements
- PhD in Computer Science or a related discipline (exceptional MSc candidates will be considered).
- 4+ years of hands-on experience developing deep learning algorithms in Python.
- Experience building end-to-end deep learning pipelines: data preparation, training, evaluation, and deployment.
- Proficiency in at least one deep learning framework (e.g., TensorFlow, PyTorch).
- Excellent problem-solving skills and a research-oriented mindset.
- Industry experience in deep learning or software development is an advantage.
- Familiarity with hardware-aware model optimization.
- Experience with cloud platforms (e.g., AWS), Docker, and Linux environments.
Full job description
All you need is:
Similar jobs
Search more AI Engineer jobsBuild production-grade internal applications and AI-enabled solutions while scaling a secure, reusable AWS platform, improving DevOps workflows, and mentoring junior engineers.
As an AI/ML Product Engineer, you will transform raw AI usage data into actionable insights, owning features from conception to deployment in a fast-paced, impactful role.
Develops and integrates generative AI applications, agents, and automated workflows with enterprise systems to improve business processes, while supporting secure deployment, evaluation, and ongoing improvement.
Own the design, delivery, and continuous improvement of AI-enabled platform capabilities and products, integrating frontend, backend, and AI systems to bring workflows and agents into production.
