ML Engineer
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
We’re looking for an ML Engineer to join our Research team. You’ll bridge the gap between research and production, turning cutting-edge ML models into reliable, scalable systems that power our AI design tools. In this role, you’ll optimize model performance, build training and inference infrastructure, and work closely with researchers and product engineers to deliver AI capabilities to millions of users.
If you’re excited about building the infrastructure behind generative AI and shipping models at scale, we’d love to hear from you.
What You Will Do
- Productionize ML Models: Take research prototypes and build them into production-grade systems with low latency and high reliability.
- Optimize Performance: Profile and optimize model inference for speed, memory, and cost across GPU and cloud infrastructure.
- Build Training Pipelines: Design and maintain scalable training pipelines, data processing workflows, and experiment tracking systems.
- Scale Infrastructure: Build and operate the ML infrastructure that supports model serving, A/B testing, and continuous deployment.
- Collaborate Across Teams: Work closely with researchers on model architecture decisions and with product engineers on API design and integration.
About You
- 3+ years of experience in ML engineering, MLOps, or a related role
- Strong proficiency in Python and PyTorch (or JAX)
- Experience deploying and serving ML models in production at scale
- Familiarity with cloud infrastructure (AWS, GCP, or similar) and containerization (Docker, Kubernetes)
- Understanding of deep learning fundamentals, particularly in generative models or computer vision
- Experience with model optimization techniques (quantization, distillation, batching strategies) is a plus
- Background in distributed training or large-scale data processing is a plus
- Contributions to open-source ML tooling is a plus
Benefits
- Work on challenging problems at the frontier of AI and design
- Competitive salary and meaningful equity compensation
- Flexible hybrid work with our San Francisco office
- Generous PTO and parental leave
- Health, dental, and vision insurance
- Learning and development budget
- Conference travel and speaking opportunities