minds.ai

Education Requirements:

• BS, MS, PhD in quantitative field, such as Physics, Mathematics, Statistics, Engineering, or equivalent practical experience.

Experience Requirements:

• Experience with some form of Deep Learning, for example Image recognition, or NLP, and a solid understanding of the foundations.

• Experience with programming in Python.

• Good knowledge of statistics, data analysis, and modelling methods.

• Problem solving skills with respect to approaching a new dataset/customer request and formulating a problem.

Other Requirements:

• 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.

Responsibilities:

• The data scientist in a team has a varied role. This can include working directly with our customers to support the deployment of our products, or internally to strengthen the products themselves.

• For our customers, your work will vary from initial data exploration, to modelling and problem formation, and then finally to the Reinforcement Learning training itself.

• Subspecialties within this broad scope are encouraged.

• You will work collaboratively with our backend, product, and AI teams.

Education Requirements:

• BS, MS, in Computer Science or a related field, or equivalent practical experience.

Experience Requirements:

• 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.

• Strong programming background with extensive experience in Python.

• Familiarity with software engineering best practices.

Other Requirements:

• 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.

Responsibilities:

• 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.

Education Requirements:

• BS, MS, in Computer Science or a related field, or equivalent practical experience.

Experience Requirements:

• Deep experience with Python programming.

• Experience designing and developing backend (REST API) servers via FastAPI (or other similar solutions).

• Experience working and interaction with databases and related technologies such as Postgres, MySQL, MongoDB, SQLAlchemy, etc.

• Experience in building scalable and fault-tolerant distributed systems.

• Have worked on solutions that contain logging, monitoring and security best practices.

Other Requirements:

• 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.

Responsibilities:

• As a backend engineer you will design, develop and support the server components of our flagship reinforcement learning platform Deepsim by making it more accessible and composable.

• You will work collaboratively with our infrastructure, data science and AI teams to ensure that their needs can be serviced by the platform, be it data processing, inspection of results, training of neural networks or model serving.