Thinking Machines is a data science startup. Our vision is for the Philippines to become a global hub for data science. To do that, we create data science cultures, one organization at a time.
We’re a company made up of intellectually curious, civic-minded, forever-learning individuals. We believe that great data science products are built with care for people, and that the best way to drive inclusive innovation is to start with a diverse team.
Our field of work is incredibly dynamic, so we want to work with people who are committed to growing with us. We want to hire people who can demonstrate an ability to learn, then provide them with personalized coaching, growth opportunities, and a great working environment to get them to world-class.
As a Machine Learning Engineer, your main responsibility will be building large, scalable solutions for big data problems. You will be highly involved with the various machine learning specialization teams, transforming their work into systems that are able to handle immense amounts of data. To do this, you will be expected to approach problems from many different ways: from leveraging cloud solutions in order to process embarrassingly parallel data problems, to picking apart and writing your own algorithms to speed up computations. Through this practice, you will be cutting down processing time from literal years to mere days, hours, or even minutes, all while being within budget.
You will also be involved in creating machine learning production systems, akin to DevOps, called MLOps. Your main challenge will be building, deploying, and maintaining highly reliable and available systems, powered by the machine learning models we create, that live in real-world production environments. This is especially important for ML projects with user application components, which need to consume and interact with our machine learning models, be it through an API that serves real-time predictions, or feedback mechanisms to continually update them.
Overall, you will be an engineer specializing in handling machine learning components for parallelization and productionalization. You are expected to have a solid engineering sense to piece together solutions from all available tools, as well as strong algorithmic thinking to create one if it does not yet exist.
Since the startup space can get crazy, we are looking for someone who is up for any challenge and has the initiative to seek out ways to be useful. We move fast, and we expect you to keep up!
We’re looking for someone who meets the following profile:
Additionally, we are requiring candidates to have an internet connection speed of at least 12mbps and a viable work from home setup, as we are now operating as a fully remote company.
This position requires the following minimum technical skills and experience:
Bonus points for experience working on Google Cloud Platform, AWS, or similar cloud providers, and can demonstrate reasonable knowledge of its architecture and moving components.
Further bonus points if you have already performed ML Ops at a professional capacity.
We offer the following compensation and benefits:
If you fit this profile and we sound like the kind of people you want to work with, fill up the form below with your information and resume. After submitting the form, please expect an email from us within the next 15 minutes, detailing the next steps for your application. If you do not receive an email from us, please contact [email protected]
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