Machine Learning Research Engineer

Job description

We are building MindsDB, an open-source explainable AutoML tool. We want to give everyone the ability to make informed predictions, using state-of-the-art ML models, with just a few lines of code.

We're looking for Machine Learning experts to join us in the journey of bringing state of the art Machine Learning to everyone.


You will be part of our Machine Learning team, with a focus on designing and implementing end-to-end Machine Learning workflows, we have a strong focus on explainability, so a lot of the work you will be doing will involve making sure that the Machine Learning models that our system generates, can explain to all (including non-technical people) where they should and should not trust the model when the model predict something, why are they making such predictions and to understand what within the data used should be of interest.

Since our project is Open Source, we encourage you to take a look at it: https://github.com/mindsdb/mindsdb & https://github.com/mindsdb/lightwood.


Requirements

You are confident that you can solve the following challenge:

https://github.com/mindsdb/lightwood/issues/169


We are looking for a team member that is:


* Eager to understand things and experiment
* Good at working with minimal supervision in a remote environment
* Excited by discussing, deconstructing and challenging ideas
* Good at communicating in English, don't worry, you don't need to be a native speaker or a poet.

* Enjoys and can be confident at programing in Python 3 


Qualifications:
* Strong understanding of machine learning and artificial neural networks, for example through active research in a
related PhD program or experience in research labs.
* Experience working with Pytorch is required.
• Papers in academic conferences (NeurIPS, ICML, ICLR, AAAI or domain-specific conferences).

Most important of all, is that you actually like MindsDB and believe in what we are trying to accomplish. We want to democratize Machine Learning and we want to make it explainable, most ML solutions out there focus on just making predictions, we also focus on the question:

* What is interesting in my data and why?
* When should I not trust this model and why?
* How can I improve this model?
* Why did the model give this prediction?


Our Commitments:

MindsDB is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees and contractors. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual
orientation, or any other characteristic protected by applicable laws, regulations and ordinances.
If you need assistance or an accommodation due to a disability, you may contact our HR Manager at abi@mindsdb.com.


Our international team of employees and contractors work remotely across numerous time zones. MindsDB recognises that a better work-life balance can improve employee motivation, performance and productivity, and reduce stress. Therefore the organisation wants to support its employees to achieve a better balance between work and their other priorities, such as caring responsibilities, leisure activities, further learning and other interests. The organisation is committed to agreeing any flexible working arrangements, provided that the needs and objectives of both the organisation and the employee can be met - we encourage candidates to discuss their flexibility needs at interview.