First and most importantly: our mission is to bring transparency and clarity to the world's data.
Our platform, Voxel51, is where AI work happens. Our enterprise platform is the mission critical linchpin for managing unstructured data, model development, and AI systems at the world's largest companies.
Our software massively impacts AI work across almost every vertical: from self-driving cars to medical imaging to revolutionizing agriculture, we are at the thrilling center of real-world AI advancement’s next wave.
And we’re built on three key tenets:
- We are all human beings: we strive to be a “human-first” organization and treat everyone with the respect, care, and flexibility that all people deserve.
- We are distributed: we believe in getting autonomy and power into the hands of people actually doing the work.
- We believe in the power of community.
We are fully remote, hiring for people based in the United States, and who are prepared to travel to an occasional in-person retreat.
About your role
As a Senior Infrastructure Engineer at Voxel51, you will shape the architecture and strategy of the systems that power our platform — from individual researchers to enterprise-scale deployments. You’ll lead the design of containerized systems, CI/CD pipelines, and deployment solutions across cloud and on-premises environments, while solving the unique challenges of serving unstructured data (images and video) at scale.
You’ll partner with enterprise customers, guiding and troubleshooting their production deployments. You’ll collaborate across engineering teams to improve developer productivity, and mentor peers while setting infrastructure best practices. Your work will directly shape the reliability, security, and scalability of Voxel51’s platform — and accelerate our mission to democratize data-centric ML.
What you will do
- Shape the architecture and evolution of Voxel51’s infrastructure to support deployments ranging from individual researchers to Fortune 500 enterprises
- Design, build, and scale deployment systems across cloud (GCP, AWS, Azure) and on-premises environments, ensuring reliability, security, and repeatability
- Partner with enterprise customers (and our Customer Success Machine Learning Engineers) to deliver and support production-grade deployments in their environments, guiding them through installation, troubleshooting, and scaling
- , enabling peers to develop, test, and ship features faster with robust internal tooling and automation
