We’re now looking for a Data Engineer or a Senior Backend Software Engineer (sometimes called Data Infrastructure Engineer, Data Platform Engineer, or Machine Learning Platform Engineer) who can lead the charge in developing and maintaining the platform that will support large-scale ML deployments. Imagine that you have cutting-edge machine learning models, but you now have to deploy them behind a bank’s four walls on a system that could be used by over 30,000 companies simultaneously in a database with billions of records. You must have 3+ years of production-level experience working with Kubernetes.
Our mission is to build financial management technologies that enable the world’s most important companies to grow more quickly in a sustainable way that’s good for people, the planet, and business.
When companies have strong cash flow performance they can shift from short-term acrobatics to long-term growth and innovation. These are the teams that change the world by being freed to optimize for all of their stakeholders, including their employees, business partners, and environment.
Cash flow is the toughest financial statement to understand but it’s fundamental to funding your own growth. We build the most intuitive and actionable tools for companies to optimize cash flow performance. Our platform analyzes billions of dollars of B2B transactions each year, users spend 70% of their workday in Tesorio, and we save finance teams thousands of hours. As a result, they can invest more confidently and anticipate their capital needs further in advance.
We’re growing quickly and working with the world’s best companies and the largest bank in the US. We recently raised a $10MM Series A led by Madrona Venture Group and are backed by top investors including First Round Capital, Y Combinator, and Floodgate. We’re also backed by tenured finance execs, including the former CFOs of Oracle and NetSuite.
We’re now looking for a Data Engineer or Senior Backend Software Engineer who can lead the charge in developing and maintaining the platform that will support large-scale ML deployments. This project you are joining is fast-paced and for a large bank, so you must be experienced—you will not have time to simultaneously onboard, gather business context, and deliver on the tight timeline. To give you a sense for the project, imagine that you have cutting-edge machine learning models, but you now have to deploy them behind a bank’s four walls on a system that could be used by over 30,000 companies simultaneously in a database with billions of records.
The ideal candidate for this role is NOT someone that can build a great model, rather you are good at building and maintaining a complex piece of infrastructure on Kubernetes and understand its common pitfalls. You should be strong at Python and SQL, a good communicator, and should be extremely reliable, able to own deliverables without dropping the ball. You must have 6+ years of experience as an engineer with 3+ years of production-level experience working with Kubernetes.
Our team is based in the San Francisco Bay Area, and we have a diverse, distributed workforce in five countries across the Americas. We don’t believe that people need to sacrifice being close to their families and where they’d prefer to live in order to do their best work.
- You will be responsible for creating and maintaining machine learning infrastructure on Kubernetes
- Build and own workflow management systems like Airflow, Kubeflow, or Argo. Advise data and ML engineers on how to package and deploy their workflows
- Implement logging, metrics and monitoring services for your infrastructure and container logs
- Create Helm charts for versioned deployments of the system on client premises
- Continuously strive to abstract away infrastructure, high availability, identity and access management concerns from Machine Learning and Software Engineers
- Understand the product requirements and bring your own opinions and document best practices for leveraging Kubernetes
- 6+ years of experience in creating and maintaining data and machine learning platforms in production
- Expert-level knowledge of Kubernetes like various operators, deployments, cert management, security, binding users with cluster and IAM roles, etc.
- Experience dealing with persistence pitfalls on Kubernetes, creating and owning workflow management system (Airflow, Kubeflow, Argo, etc.,) on Kubernetes
- Experience creating Helm charts for versioned deployments on client premises
- Experience securing the system with proper identity and access management for people and applications.
- Ability to work in a fast-paced, always-changing environment
Nice to have: Experience spinning up infrastructure using Terraform and Ansible
Nice to have: Experience working with data engineers running workflow management tools on your infrastructure