About the role
ML Engineer – Careers – QuiverAI Skip to main content $8.3M seed led by a16z Announcing our $8.3M seed funding round led by a16z | Read the blog post Solutions Developers Enterprise Pricing Company Careers Blog Get started Sign in Get started Careers / Research ML Engineer Research · Full-time · Hybrid (San Francisco) Apply for this role About QuiverAI QuiverAI is building the future of AI-powered design tools. We help designers and teams create stunning vector graphics, illustrations, and brand assets with the power of generative AI. Our mission is to democratize great design by making professional-quality creative tools accessible to everyone.
About
the Role QuiverAI helps creative teams move from idea to output faster. We are looking for an ML Engineer who can bridge research and production, turning strong model work into reliable systems for training, inference, and serving. You will work on problems that matter alongside researchers and engineers, helping turn frontier model work into reliable systems with real-world impact. This is a hybrid role based in San Francisco. What You Will Do Profile and optimize model inference for speed, memory, and cost across GPU and cloud environments. Take research prototypes and turn them into production-grade systems with low latency and high reliability. Build and maintain scalable training pipelines, data processing workflows, and experiment tracking systems. Build and operate ML infrastructure for model serving and continuous deployment. Work closely with researchers on model architecture decisions and with product engineers on API design and integration. What We’re Looking For 3+ years of experience in ML engineering, MLOps, or a related role. Strong proficiency in Python and PyTorch, plus familiarity with modern ML engineering tooling. Experience deploying and serving vision-language models in production at scale. Familiarity with cloud infrastructure and containerization, including Docker and Kubernetes. Strong understanding of deep learning fundamentals, especially in generative models or computer vision. Experience with model optimization techniques such as quantization, distillation, or batching strategies. Background in distributed training or large-scale data processing is a plus. Contributions to open-source ML tooling are a plus. What We Offer Competitive