United States onlyWe’re looking for an AI Engineer to design, implement, and optimize advanced AI systems that balance quality, performance, and cost. You’ll work on inference pipelines, retrieval-augmented generation (RAG), and multi-agent patterns while building evaluation harnesses and simulation-based training frameworks. This role combines deep technical skill with a strong focus on real-world reliability and user experience.
System Design: Architect inference and RAG pipelines; design manager/worker agent patterns with deterministic fallback mechanisms for reliability.
Evaluation & Training: Build robust evaluation harnesses and develop simulation-based training pipelines to improve model robustness.
Optimization: Tune AI systems across quality, latency, and cost dimensions, ensuring scalable production performance.
Experimentation: Run A/B tests and continuous feedback loops to measure system performance and guide improvements.
User Experience: Collaborate with product and design teams to ensure AI outputs integrate into workflows with intuitive, high-quality UX.
Requirements
- Strong background in machine learning, AI systems, or applied research.
- Hands-on experience with inference optimization and retrieval-augmented generation (RAG).
- Familiarity with multi-agent or distributed system design.
- Experience building evaluation frameworks, test harnesses, or simulation-based training environments.
- Skilled in performance tuning (latency, throughput, cost efficiency).
- Proficiency in Python and ML/AI frameworks (PyTorch, TensorFlow, LangChain, Ray, etc.).
- Bonus: Experience with A/B testing, feedback loops, and integrating AI with front-end UX.
