AI Engineer

The Role

As an AI Engineer at Normal, you will build production systems that understand AI model architectures, technical documents and chip design & verification collateral like reference models, testbenches, FPGA emulation or analog simulations , and help build autoresearch loops, verifiers and infrastructure to optimize the design process.. You'll ship real improvements to our production platform while pushing the boundaries of what's possible in AI for hardware through reinforcement learning, agentic coding, and exceptional software engineering.

What You Will Own

• End-to-End Development: Lead AI development from initial concept through production deployment and iteration.
• Agentic Systems: Handle multi-modal complexity and explore multi-agent, autoresearch and RL approaches for agentic code generation and tool use, interacting with both physical and simulated rewards.
• Production Quality: Design strategies to manage latency, output variance, and graceful error handling at scale.
• Platform Integration: Collaborate with product and engineering teams to embed AI capabilities seamlessly into our platform.
• Mentorship: Guide junior engineers and establish best practices for AI development.

What Makes You a Great Fit

• Previous experience delivering production AI systems involving language models, preferably involving agentic coding workflows & tool use.
• Solid software engineering skills with experience in distributed systems, job scheduling and production-grade code
• Proficiency in Python and modern ML frameworks (PyTorch, Hugging Face, transformers)
• Experience with benchmarking and profiling different ML models, runtime configurations (VLLM/SGLang) and hardware architectures.
• Hands-on experience with prompt engineering, fine-tuning, and deploying large language models
• Ability to explain complex AI concepts to both technical and non-technical stakeholders

Bonus Points

• Experience with hardware design, hardware verification, or EDA tooling
• Experience deploying AI systems in mission-critical or high-stakes production environments
• Experience with cloud platforms (AWS, GCP, Azure) for large-scale AI infrastructure
• Research or applied experience with LLM agents, RL (offline/online, RLHF/RLAIF), constrained decoding, or program synthesis
• Open-source contributions or publications in AI/ML venues
• Skill in balancing cutting-edge innovation with production reliability and pragmatism