Python Job: Graduate | Machine Learning Engineer

Please submit your CV and cover letter to [email protected].

Machine Learning Engineer

To advance Mindhive by developing algorithms to build models that uncover connections and make better decisions without human intervention. Mindhive has started to reimagine intelligence. Forming a global mind from the most unusual suspects, who were in it for the ideas, not the glory. And finding a way that technology could enhance human minds, not replace them.

More here: https://www.web.mindhive.org/careers

ROLE

Experimentation is at the core of what you do. The role is to work to turn business questions into data analysis effectively and provide meaningful recommendations. This unique hybrid role will focus on your data infrastructure knowledge and your ability to drive insights.

AUTHORITY

Develop models and train them

Find and implement new technologies

RESPONSIBILITY

Architect, build, maintain, and improve new and existing suite of algorithms and their underlying systems,

Implement end-to-end solutions for batch and real-time algorithms along with requisite tooling around monitoring, logging, automated testing, performance testing, and A/B testing,

Utilise your entrepreneurial spirit to identify new opportunities to optimise business processes and improve consumer experiences, and prototype solutions to demonstrate value with a crawl, walk, run mindset,

Establish scalable, efficient, automated processes for data analyses, model development, validation and implementation,

Write efficient and well-organised software to ship products in an iterative, continual-release environment,

Contribute to and promote good software engineering practices across the team,

Knowledge sharing with the team to adopt best practices,

Actively contribute to and reuse community best practices.

REQUIREMENTS

Web App development experience.

End-to-end python Machine Learning experience.

NLP experience with Machine Learning

University or advanced degree in engineering, computer science, mathematics, or a related field,

5+ years experience developing and deploying machine learning systems into production,

Strong experience working with our stack

Industry experience building innovative end-to-end Machine Learning systems,

Ability to quickly prototype ideas and solve complex problems by adapting creative approaches,

Experience working with distributed systems, service-oriented architectures and designing APIs,

Strong knowledge of data pipeline and workflow management tools,

Expertise in standard software engineering methodology, e.g. unit testing, test automation, continuous integration, code reviews, design documentation,

STACK

Mindhive uses a reasonably standard Model-View-Controller architecture based on Postgres-Django-Python in the back end and Vue.js in the front end. Application infrastructure is hosted on AWS, and Cloudflare protects from DDoS attacks.

Mindhive is currently engaged in several initiatives:

Transitioning our AWS environment to “infrastructure as code” based on Terraform. We are taking advantage of this transition to introduce improvements - e.g., streamline updates, agility to plug in AWS smarts, etc.

We are working towards ISO 27001 compliance. This exercise has prompted sustainability initiatives, including the one above.

Code is in BitBucket with CI/CD pipelines. When our “infrastructure as code” initiative is complete, the entire Mindhive stack should be able to be recreated at the push of a button.

Also, the application Yeah-Nah is coded in Flutter and AWS-Amplify, providing a segue-way for the organisation to move into smartphone apps.

Python developers write test cases into the Python code.

We currently use Kubernetes. However, this has proven a little complex for a small team to manage so the new Terraform-based environment won’t be using it.

Serverless: We already have our toe in the water here (AWS Amplify, two helper functions to do with scanning downloads for malware) and anticipate moving further in that direction when appropriate.

Development environment on developer’s workstations (using Docker). Staging and Production on AWS.

Use Datadog and Sentry for logging and monitoring.

User authentication against Auth0.

Frontend increasingly uses APIs to communicate with the backend.

Emphasising the use of loose coupling, increasing agility.