Senior Infrastructure Engineer, Voxel51
$210-240k
+ Equity
Data platform for computer vision models
Open for applications
Data platform for computer vision models
21-100 employees
Open for applications
$210-240k
+ Equity
21-100 employees
Company mission
Voxel51's mission is to bring transparency and clarity to the world's data through its computer vision data modelling platform.
Role
Who you are
- Deep experience with containerized environments
- Building, packaging, and debugging container images
- Kubernetes (and Docker Compose) for orchestration
- Building, maintaining, and deploying Helm charts
- Infrastructure as Code expertise (Terraform, Ansible, or equivalent)
- Scripting and automation skills (Bash or similar)
- Python expertise, including build and environment management, packaging/distribution, release management, and dependency debugging
- CI/CD systems experience, ideally GitHub Actions (we use this today)
- Cloud infrastructure knowledge, especially GCP (IAM, VPC, load balancing, ingress/egress routing, proxies, firewall rules)
- Database fundamentals, ideally MongoDB or similar NoSQL systems
- Observability skills, including designing meaningful monitors, logging, tracing, and alerting
- Security best practices, including certificates, service accounts, least privilege, and role assumptions
- Troubleshooting ability across complex, distributed systems (including with customers in the loop)
- Testing mindset: comfortable with designing and applying different types of tests to validate functionality
- Strong communication skills, with the ability to work directly with enterprise customers as well as collaborate across teams in a remote-first, collaborative environment
- Adaptability and curiosity, with the ability to ramp quickly on unfamiliar concepts and technologies
What the job involves
- 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
- 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
- Lead infrastructure initiatives across engineering teams, enabling peers to develop, test, and ship features faster with robust internal tooling and automation
- Drive best practices in CI/CD, evolving our pipelines (currently GitHub Actions + Google Cloud Build) and introducing new approaches where they add value
- Develop and maintain deployment solutions for Voxel51-hosted environments (GKE) as well as customer on-prem installations (K8s or Docker Compose)
- Champion developer productivity, improving workflows for development and automated cloud deployments
- Troubleshoot and resolve complex infrastructure issues, spanning build failures, runtime failures, and customer deployment challenges
- Anticipate and prevent failures by designing monitoring, alerting, and predictive solutions for both internal and customer environments
- Mentor engineers and set technical direction, ensuring Voxel51’s infrastructure remains ahead of customer needs and industry trends
Our take
Recent innovations in computer vision mean that AI can recognise images within large-scale datasets, presenting significant opportunities for innovation in just about every industry. However, this only works successfully when AI is trained with high-quality datasets, and it is difficult to organize, locate, and correct mistakes at such a high scale.
Voxel51 provides a data platform for computer vision models called FiftyOne, an open-source toolkit that helps users automate the organization, navigation, and creation of computer vision models, regardless of size. The platform also includes automated data quality control tools to help users identify detrimental mistakes within models, a process that is necessary for successful computer vision.
Many startups are tackling inefficient computer vision workflows, but Voxel51 has found a gap in the market by focusing specifically on the laborious manual processes associated with organising and aggregating high-quality datasets. It has formed a collaborative partnership with Google, empowering its platform with Google's pre-annotated image datasets, and in 2023, released an AI assistant that can generate Python Code for computer vision dataset analysis. 2024 funding is being used to scale up and accelerate its AI research.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
Company
Funding (last 2 of 4 rounds)
May 2024
$30m
SERIES B
Sep 2022
$12.5m
SERIES A
Total funding: $45.8m
Company benefits
- Medical, Dental & Vision
- Flexible work schedules
- Generous PTO
- Family-friendly policies
- Stock options
- 401k matching
- Flexible set-up
- Rich team building
- Healthy remote playbook
Company HQ
Ann Arbor, MI
Leadership
Jason Corso
(Co-Founder & Chief Science Officer)Experience as Director of the Stevens Institute for Artificial Intelligence. Associate Professor of Electrical Engineering and Computer Science at the University of Michigan since 2014.
Brian Moore
(Co-Founder & CEO)Was a Software Engineering and Research Intern at Google. Also a Graduate Research Assistant at the University of Michigan from 2012 to 2017.
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