Senior data and ML engineer

• Preferably one strong project utilizing either NLP or computer vision stacks.
• Preferable if you can share a portfolio of work that you can show off -- either on Github, Kaggle, your personal webpage, or something similar. 
• Other details
• Probation. Let's make it easy for both of us to find out if we enjoy working with each other. We will give ourselves two months - and we get to decide whether we want to continue working with each other after that. Your salary etc. will be unchanged during this time period.
• Flexible place of work. We understand that every engineer has their own preference in working. We have office spaces in Kathmandu and Bangalore -- if you're not comfortable working from home, feel free to use this office space. For the first two months, let us both see what works best for us -- if we find that you benefit from slightly focused guidance, we will insist that you come to office for a few days a week. If we find that you are very successful and are producing high quality results despite being independent and fully remote, then feel free to continue working remotely.

Your responsibilities, depending on your seniority, will mostly include the full pipeline of any data/machine learning project:

Collecting data from various disparate sources

• Analyzing collected data
• Visualize and communicate that information to stakeholders.
• Design and train learning models on it.
• Architect and implement the ML-ops aspect of the models---so that the model can be used in production
• Quick prototyping of products to show off how such trained models can be used.
• Mentoring younger engineers
• If interested, engage with the pre-sales, sales conversations on the product. This will give you a sense for how these products are taken to market.
• Documenting and publishing your work on the organization's technical blogs.

Depending on the project, you may be required to spend your time on one subset of the listed responsibilities. Please be patient - and you will get to grow and learn as an engineer. We want to work with engineers who are open to experiencing this whole pipeline and not just one particular subset of the listed responsibilities. 

While everyone may relate to these desirable skills, we will evaluate how well you align with them by looking at your past work, and by getting you to engage on an open-ended task. Focus on showing us evidence for an alignment with the skills we have listed above. For example, if you are curious about different technologies, you could have written about what you've learned on a blog post--- share that link with us. If you enjoy autonomy, you could have worked on a project by yourself and analyzed some results from it---share its github repository, containing a well documented readme file. 

• curious: about the different machine learning tools and stacks available. You want to learn by trying out a diverse set of these technologies.
• a go-getter: loves to get things done; has a "let's do it" attitude, and is willing to try out things, fail, prototype, and improve their skill sets.
• sincere and conscientious: it is important you hold yourself to a high bar, have a keen attention to small details, and are sincere to yourself and the work you do.
• proudly owns their work: We want you to own the work you pick up. Invest your thoughts in your work as if the success of the organization depends totally on it.
• keen on learning what it takes to build and ship successful ML technology: You will see that it takes much more than putting together a few libraries.
• enjoys autonomy: you prefer trying out 10x more choices than what your mentor suggested. You will thrive and be productive despite not being spoon-fed how to exactly solve a problem. Startups and young engineering teams generally do not have the bandwidth to provide a lot of handholding. 

Note: This role does not prioritize research as its primary focus. Our goal is not to produce scientific reports. We're focused on building state-of-the-art products and systems, which will often require complex engineering, and which will utilize insights derived from the latest scientific literature.