engineering VS MLServer

Slim.AI | Fullstack and Backend Engineers | REMOTE, international or Seattle/Bellevue/WA | Full-time | Golang, Node.js, Vue.js/Nuxt.js

I'm the founder and CTO at Slim.AI. We are a well funded seed stage startup (9M+) in the developer tooling space. Our mission is to simplify and accelerate the containerized app delivery (it's too hard, too complicated and with too much manual work). We are about to transition to the next phase and we are expanding our engineering team.

Our engineering team is the innovation engine for our product because we are building a solution to solve our own problems creating and running containerized cloud-native applications.

We use Golang, Node.js Serverless/Lambda and containers. We have frontend, backend and fullstack roles ( https://github.com/slim-ai/engineering ).

Our engineering principles:

* We use what we build.

Slim.AI | Backend and Fullstack Engineers | REMOTE, international or Seattle/Bellevue/WA | Full-time | https://github.com/slim-ai/engineering

We are a well funded seed stage startup (9M+) in the developer tooling space on a mission to redefine how DevOps is done for containerized apps (it's too hard, too complicated and with too much manual work). We are about to transition to the next phase and we are expanding our engineering team.

Our engineering team is the innovation engine for our product because we are building a solution to solve our own problems creating and running containerized cloud-native applications.

We use Golang, Node.js Serverless/Lambda and containers. Take a look at the backend ( https://github.com/slim-ai/engineering/blob/master/roles/bac... ) and fullstack ( https://github.com/slim-ai/engineering/blob/master/roles/ful... ) roles and our engineering principles to see if the role and how we do engineering looks interesting to you ( https://github.com/slim-ai/engineering#engineering-principle... ).

Email me at [email protected] if you'd like to learn more.

P.S.

And take a look at DockerSlim ( https://github.com/docker-slim/docker-slim ) if you are interested in working on the open source project that powers our SaaS.

Slim.AI | REMOTE or Seattle | Full-time | Developer Experience Lead | https://github.com/slim-ai/engineering

Do you enjoy working with lots of different applications stacks? Do you like helping others? Do you want to build lots of different applications? Are you interested in contributing to open source?

We are a funded seed stage startup in the developer tooling and DevOps space empowering developers to build and run their cloud-native applications. The current product is focusing on containers and the friction around them.

We are building a brand new engineering team. We are developer friendly, low on process with no mind-numbing bureaucracy or micromanagement. We are looking for people who'll be excited to be a part of the engineering team in an early stage startup during its inception phase building modern cloud-native applications the right way.

You can find out more about the mission, how we work and the roles here: https://github.com/slim-ai/engineering

Email me at [email protected] if you'd like to learn more.

You've already mentioned Seldon Core which is well worth looking at but if you're just after the raw multi-model serving aspect rather than a fully-fledged deployment framework you should maybe take a look at the individual inference servers: Triton Inference Server and MLServer both support multi-model serving for a wide variety of frameworks (and custom python models). MLServer might be a better option as it has an MLFlow runtime but only you will be able to decide that. There also might be other inference servers that do MMS that I'm not aware of.

Ever trained a new model and just wanted to use it through an API straight away? Sometimes you don't want to bother writing Flask code or containerizing your model and running it in Docker. If that sounds like you, you definitely want to check out MLServer. It's a python based inference server that recently went GA and what's really neat about it is that it's a highly-performant server designed for production environments too. That means that, by serving models locally, you are running in the exact same environment as they will be in when they get to production.

Seldon | Multiple positions | London/Cambridge UK | Onsite/Remote | Full time | seldon.io

At Seldon we are building industry leading solutions for deploying, monitoring, and explaining machine learning models. We are an open-core company with several successful open source projects like:

* https://github.com/SeldonIO/seldon-core

* https://github.com/SeldonIO/mlserver

* https://github.com/SeldonIO/alibi

* https://github.com/SeldonIO/alibi-detect

* https://github.com/SeldonIO/tempo

We are hiring for a range of positions, including software engineers(go, k8s), ml engineers (python, go), frontend engineers (js), UX designer, and product managers. All open positions can be found at https://www.seldon.io/careers/