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How did you get to where you are today? What is your story? What incidents and experiences shaped your career path?

Summarized By: Jeff Musk on Sat Dec 21 2019
I started as a computer science major. And I wanted to do more research on maybe getting to good academia. I started doing PhD in actually, it was, I was first working on networking, and I was really interested in machine learning. So I switched my area to machine learning and did a PhD in UC, Riverside. I worked on some applied projects related to health care during my PhD towards the last few years, and that got really got me interested in doing applied research on health care and I wanted to do more of that. And there was an interesting project at Amazon that was related to applying machine learning to health care. So I joined that project, and I've been working on that project in the last few years.

What are the responsibilities and decisions that you handle at work? Discuss weekly hours you spend in the office, for work travel, and working from home.

Based on experience at: Machine Learning Scientist, Amazon Web Services (AWS)
Summarized By: Jeff Musk on Sat Dec 21 2019
I'm working as an applied scientist at Amazon and the responsibilities are refiltering the latest machine learning papers on the research that's going on. And, my team is working on mainly on the nature of language processing. So we get together as this team of scientists and read papers and talk about different areas of research and what we can do for the product which is named into recognition and relation extraction service for healthcare-related documents. So we read papers and implements them in deep learning frameworks, and write scripts to test, evaluate work with engineers to productionize those research applications and integrate them into the product. Also, a lot of my time is spent on coding, implementing reading papers and also working with engineers. So I work mostly in the office and I go to conferences usually twice a year. And then it's working from home is that we have a lot of meetings, so we don't usually work from home a lot. But if we want to, we can always work from home. That's something okay to do but maybe not so frequently. 

What tools (software programs, frameworks, models, algorithms, languages) do you use at work? Do you prefer certain tools more than the others? Why?

Based on experience at: Machine Learning Scientist, Amazon Web Services (AWS)
Summarized By: Jeff Musk on Sat Dec 21 2019
We use deep learning frameworks and python usually. And there isn't like a preferred framework. We use all TensorFlow PyTorch MexNet frameworks and we implement in Python and iSight, but, engineering work is done more in Java so we're familiar with java as well. We work on deep learning algorithms for natural language processing and language models.

What things do you like about your job? Were there any pleasant surprises?

Based on experience at: Machine Learning Scientist, Amazon Web Services (AWS)
Summarized By: Jeff Musk on Sat Dec 21 2019
I'm basically still doing research a little bit, reading a lot of papers and writing some papers at the same time as well. So we tried to have some research, and not just for applying to product. Those are related to the products in some way, but they're things that could be applied at a later date. So, I like that a lot. And I like that my work is very related to health care and it has a big impact on people's lives. Also, it has a good balance of application and research. So and I can still go to conferences so those things air really good. And there are a lot of really good scientists that I can learn from as well. So we have a really good team and different teams work together. It's a big company. So there is a potential for collaboration as well

What are the job titles of people you routinely work with inside and outside of your organization? What approaches do you find to be effective in working with them?

Based on experience at: Machine Learning Scientist, Amazon Web Services (AWS)
Summarized By: Jeff Musk on Sat Dec 21 2019
So I work with, scientists mostly, but also software engineers a lot and also product managers a lot. So engineers will productionize our prototypes and we're closely interacting with the product managers so that together shape the product that customers will like more. And, what approaches I find effective, working with them. So it's we have some meetings where we present our work to the whole team. And we have a lot of meetings to discuss what's going on, weekly meetings to know what everyone is working on. And we try to be really involved in each other's work to help each other. So I find that you know, doing presentations on the work you do really helps to showcase, but also get other people involved like other engineers.

What major challenges do you face in your job and how do you handle them? Can you discuss a few accomplishments?

Based on experience at: Machine Learning Scientist, Amazon Web Services (AWS)
Summarized By: Jeff Musk on Sat Dec 21 2019
A major challenge that I faced at first was natural language processing was a little outside with my expertise. So that was a new area for me, but, I was able to learn a lot from the other scientists in the team. And all the journal clubs reading different papers together, discussing those papers together, helped a lot to learn more and get caught up on the new research in NLP. The other major challenges were the pace of course like productionizing is a different thing that I hadn't done in PhD. So you have to have a prototype, but you have to also test it and evaluate it a lot like more thoroughly because that will go into on a large scale, and be used by a large number of customers. And I think I learned a lot from the other teammates and figured more as I went forward. 

What are the recent developments in the field? How significant are these improvements over past work? What are their implications for future research & industry applications, if any?

Based on experience at: Machine Learning Scientist, Amazon Web Services (AWS)
Summarized By: Jeff Musk on Sat Dec 21 2019
There's a big trend like it happened in computer vision. There's a big kind of pre-training in language models, label that large amounts of unlabeled data that's available either like Wikipedia or books or other scientific publications. So that we can use those contextual words embedding those language models and many other tasks like Bird, Roberto, QPT and a lot of different models are still coming out and they're getting larger, bigger, and they're usually state of the art. And a lot of NLP tasks. They have significant improvements and especially some tasks. And I think the implications for future research are trying to find more and more generalized algorithms that can fit many different tasks and get away from really specific algorithms. So I think that's good trends that have a lot of implications. 

What was the hiring process like for your job? What were the roles of people who interviewed you? What kind of questions were asked?

Based on experience at: Machine Learning Scientist, Amazon Web Services (AWS)
Summarized By: Jeff Musk on Sat Dec 21 2019
I was interviewed by I think two or three scientists So I think maybe three scientists one engineer and one hiring manager. Science questions are related to my PhD research, my papers on also use case questions like, how would you design a machine learning system to solve the question like a general question. And you're expected to ask questions and clarify and come up with a mission learning algorithm. But also, you're expected to answer questions like with the data, how would you process it and everything related to that. And on the engineering side, it was like normal engineering questions. And there were some behavioral questions, like about previous experiences, how do you handle ambiguity and other behavioral questions like that.

What qualities does your team look for while hiring? How does your team interview candidates?

Based on experience at: Machine Learning Scientist, Amazon Web Services (AWS)
Summarized By: Jeff Musk on Sat Dec 21 2019
We look for different for each position. But we are really a mission learning heavy team. So we look for some knowledge ono machine learning, especially on deep learning methods. And even if someone might not have the related experience. But we look at the relevant experience to see if someone can learn and start working on and producing good work in a short amount of time. So someone who is open to learning new things and someone who has atleast some relevant knowledge on machine learning and coding is also very important. There is a coding bar that everyone has to, no matter what the position, pass as well. And yeah, we usually have a few forms screen first, and then we have an on-site after that to interview the candidates. And there's always someone outside the team at Amazon who is also is a part of the interview so that they look at it not just from the team perspective, but can this person work in any other team in the Amazon.

What are different entry-level jobs and subsequent job pathways that can lead students to a position such as yours?

Based on experience at: Machine Learning Scientist, Amazon Web Services (AWS)
Summarized By: Jeff Musk on Sat Dec 21 2019
For a scientist position, the minimum entry-level of requirement might be a master's degree or someone who has a few years of industry experience. So I think, either like, masters might be a good way to get some machine learning experience and knowledge. And another way is to start as a software engineer. I think there are a lot of entry-level software engineer positions and then getting involved in machine learning projects and basically moving more and more into a machine learning engineer type of position as well. And then from there, you can also become a scientist.

How did the program prepare you for your career? Think about faculty, resources, alumni, exposure & networking. What were the best parts?

Based on experience at: University of California, Riverside
Summarized By: Jeff Musk on Sat Dec 21 2019
I took a lot of machine learning related courses, of course, helped with the foundations. And I think the best part was the my partner in my lab. There were other professors and the lab as well and their students. So the lab atmosphere and working together with the other lab mates and my advisors' guide really helped me and it also helped me networking as well. So I think those were the best parts. 

Do you have any parting advice for students and professionals starting out in your field? What three mistakes they should avoid? What three things would help them the most?

Based on experience at: Machine Learning Scientist, Amazon Web Services (AWS)
Summarized By: Jeff Musk on Sat Dec 21 2019
So I would advise finding internships. I think that's one of the most important things. Or working on some projects with professors, even as an undergrad student start doing some research, writing papers or finding internships and getting involved in the projects at a company. One of those things would help a lot to both meet the network with a lot of people and get a lot of hands-on experience. I think that those are the two most important things. And then mistake to avoid. I can't think of any at the top of my head. But I think getting hands-on experience either, as a part of working out with a professor or an internship would help the most.