Lead Infrastructure Engineer
About the role
Please Apply Here: https://career.mindsdb.com/o/lead-infrastructure-engineer
We’re looking for someone that’s (really really) good at infrastructure, more specifically at large scale distributed python-based workloads and multi-machine parallelization. Someone that’s very familiar working with AWS and ideally knows or can quickly learn his way around other IaaS providers. Someone that’s an expert when it comes to python and linux, and someone that’s solved difficult problems around the area of “system programming.”
You will handle development of MindsDB’s cloud service, a product that aims to provide infrastructure on demand and distribution of workloads for MindsDB, an Open Source automatic machine learning library we are developing. Within we year we expect our cloud to grow to support hundreds of customers with thousands of machines.
In addition to the core experience mentioned above, it would be rather nice if you had experience working with ray, pytorch and a solid understanding of machine learning, from a high level applied perspective.
Most of all though, we are looking for someone that is proactive in their work, able to both find issues and fix them, able to both follow the company’s vision and contribute to it.
This role is currently one where you will be working alone, but able to request help from various team members. As Mindsdb cloud and our infrastructure requirements in general grow, this will develop into a team lead role.
In this role you’ll get to:
- Design and implement parallel and distributed computing solutions for state of the art machine learning.
- Take ownership of MindsDB’s infrastructure code.
- Be an active part of a team that works across stack and gain understanding of MindsDB from the frontend all the way to the inner workings of our ML models.
- You played a key role as an engineer in large infrastructure projects.
- You know the ins and out of AWS, specifically EC2.
- You are a stellar python developer.
- You are very comfortable working with linux (e.g. given a week you could have a production-ready server using LFS.)
- You understand core concepts behind distribution and parallelism at a theoretical level and have worked on applied parallel and distributed computing problems.
- Excellent written and verbal communication in English.
- You are familiar with current IaaS GPU offerings across dozens of providers.
- You know how to use terraform.
- You’ve worked with or understand ray and pytorch (with a focus on getting CUDA to work efficiently on varying hardware.)
- You have a good understanding of everything security and can balance having a relatively safe system while prioritizing ease of usage by the team, not introducing any ritualistic/theatrical security.
More about our benefits
Whether you work in an office, remotely, or a distributed team, MindsDB is highly collaborative and yes, fun! To support you at work (and play) we offer some fantastic perks: ample time off to relax and recharge, flexible working options and lots more.
MindsDB is an equal opportunity employer
All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations, and ordinances.
More about us
MindsDB helps anyone use the power of machine learning to ask predictive questions of their data and receive accurate answers from it. MindsDB was founded in 2017 by Adam Carrigan (COO) and Jorge Torres (CEO), is backed with over $5.2M in seed funding from the University of California, Berkeley SkyDeck fund, OpenOcean, and the co-founders of MySQL and MariaDB. MindsDB is also a graduate of Y Combinators’ recent Winter 2020 batch.
Why you should join MindsDB
MindsDB is an open-source platform designed to accelerate machine learning workflows by generating predictions at the data layer with virtual AI Tables so you can consume predictions as regular data and query them with standard SQL, reduce development and deployment complexity using AutoML, and improve insight into machine learning model accuracy - increasing prediction confidence.