Hop
Staff Engineer, Agentic Models
Dallas, TX · Staff+
Sponsorship not specifiedDetected 93 days ago
PythonC++AlgorithmsGitTensorFlowPyTorchRAGAgentic AIEmbedded SystemsAdaptability
About the role
- If you're a builder with a passion for AI, a global semiconductor company wants you on our SoC AI team as a Staff Engineer focused on agentic models.
- Here, you'll gain unique experience integrating AI models with both software and hardware, learning how to make autonomous systems function seamlessly across the entire stack.
- You'll take models from initial concept through training, fine-tuning, and RL, deploying them for agentic systems.
Responsibilities
- Own the full lifecycle of agentic AI models: architect, train, fine-tune, and optimize it for AI agents.
- Problem solver and communicator - ready to connect, share, and lead in a diverse, cross-functional team.
Nice to have
- Strong grasp of optimizing AI on any hardware for strict resource budgets.
- Track record of launching real-world models and AI-powered products that users love.
- You'll be at the epicenter of game-changing AI innovation, driving the evolution of intelligent embedded systems that are poised to transform entire industries.
- Join forces with brilliant engineers, tap into breakthrough technologies, and play a pivotal role in revolutionizing automotive, industrial automation, and IoT.
- Here, your contributions won't just move the needle - they'll redefine what's possible and shape the future of smart devices worldwide.
- Experience in autonomous agent modeling, RL, adaptive algorithms, and frameworks like RAG, persistent memory, and multi-agent collaboration.
- Fluent in Python and C/C++, and at home in TensorFlow, PyTorch, or similar platforms.
Company info
- We're redefining how autonomous systems interact with the real world, and we need creative minds who thrive in rapid-paced, mission-driven environments.
- We're laser-focused on innovation and adaptability - here, your ideas will shape the next wave of smart, agentic devices.
This listing is sourced directly from Hop's careers page and normalized into a canonical job model.