minds.ai (pronounced as mind's eye) has developed a distributed reinforcement learning platform called DeepSim. This platform is deployed at our customers to solve complex optimization problems in verticals such as renewable energy, manufacturing process optimization, automotive, etc. We received multi million venture capital investments to build out our platform and customer base.
We value excellent engineering skills and a drive to learn. We consider these traits as important as having prior platform development experience.
About the Role
As an infrastructure engineer you will design, develop and support our flagship reinforcement learning platform Deepsim by making it more scalable, robust, and deployable. Our customers often have very diverse deployment and MLOps requirements and DeepSim will have to support them all. You will work collaboratively with our data science and AI teams to ensure that their needs can be serviced by the platform, be it data processing or model serving.
About the minds.ai engineering team
We have a supportive engineering environment that encourages our engineers to expand their knowledge and capabilities. Senior engineers regularly provide informal instructions to junior engineers. The skillset of our team is extremely broad with people experts in specific topics such as Reinforcement Learning, GPU computing, data science, programming and cloud computing.
The things we look for
Good working knowledge of Docker, Kubernetes, Terraform, and other cloud-native technologies.
Experienced with working with public cloud providers (e.g. Azure, GCP, or AWS).
Excellent debugging skills of distributed systems software and willingness/ability to go under the hood when required.
BS, MS, in Computer Science or a related field, or equivalent practical experience.
Strong programming background with extensive experience in Python.
Familiarity with software engineering best practices.
Experience in building scalable and fault-tolerant distributed systems.
It is a bonus if you also have
Previous experience with software like Apache Airflow, Kubeflow, MLflow, etc.
Previous experience building machine learning training pipelines and infrastructure.
Experience of working with observability/logging stacks.
Exposure to libraries such as Tensorflow, PyTorch, numpy, pandas, sklearn, Ray.
Experience with multiple operating systems (Linux, macOS and Windows)
You are a fit for this role if you have
Good written and verbal communication.
Demonstrated ability to work with minimal to no supervision.
Ability to work remotely with cross-cultural teams.
Self-motivation to learn new concepts and an excellent record of execution.
A sense of urgency, result orientedness, and excellent ability to prioritize.
Our teams are centered around Amsterdam (the Netherlands), Bangalore (India), and Santa Cruz, California (USA). We are location agnostic, and team members regularly work together from remote locations. We are a small, but growing family of hand-selected experts.
We believe in extreme transparency. Every one of us in minds.ai knows the general direction of the company, current, and future customer prospects, commercial terms, etc. This enables every one of us to influence the course of the company. Debate and discussions are heavily encouraged. The minds.ai value statement is not a bunch of words but is a living document that guides all our decisions.
We organize regular training and knowledge-sharing sessions led by your colleagues on a weekly basis. If you are inclined, you can offer the same to your colleagues. Apart from work, you will be able to attend conferences and network with colleagues within the industry. We encourage you to give talks at conferences, meetups and submit papers to publications. The company will guide you through this.
As most of us work in remote locations, we put great emphasis on frequent communication. In addition to regular video chats, we strive to meet in person once a year with families.
This is how we will become a team
Our selection process is friendly and interactive. It will involve solving computer science problems, writing code, and getting to know each other. This will be done in a series of meetings either in person or on video. Typically, you can expect to attend 6 to 8 meetings. Some might say it is a tad too many, but we feel that it is essential for you and us to invest this time in the beginning.
We encourage dialogue so be prepared with all your questions and ask them without hesitation.
For more information or to apply, please contact: