6 months ago
Responsibilities
- Own the architecture, training, fine-tuning, optimization, and deployment of agentic AI models.
- Develop autonomous behaviors using reinforcement learning, adaptive algorithms, retrieval-augmented generation, persistent memory, and multi-agent collaboration.
- Collaborate with hardware, software, and product teams to integrate AI models into autonomous and embedded systems.
- Prototype and iterate on multi-agent and reinforcement-learning approaches for real-world, resource-constrained environments.
- Optimize models for speed, reliability, efficiency, and integration with embedded hardware.
- Identify applications for agentic models across edge and embedded AI systems.
Requirements
- Demonstrated hands-on experience developing AI models for agentic systems through innovative real-world projects or technical publications.
- Experience with autonomous agent modeling, reinforcement learning, adaptive algorithms, retrieval-augmented generation, persistent memory, and multi-agent collaboration.
- Fluency in Python and C/C++.
- Experience with TensorFlow, PyTorch, or similar AI platforms.
- Strong problem-solving, communication, and cross-functional collaboration skills.
Benefits
- Work on advanced AI innovation for intelligent embedded systems in automotive, industrial automation, and IoT applications.
- Collaborate with experienced engineers and work across the full software and hardware stack.
Categories
About Hop
Hop is a privately held staffing marketplace that connects AI, GenAI, and machine-learning talent from Latin America with companies worldwide. It sources, screens, and places data scientists and AI engineers using an in-house assessment platform, and complements recruiting with mentorship and training programs. Founded in 2016 and headquartered in Walnut Creek, California, Hop operates on recruiting and staffing fees for organizations building data and AI products.