Mapbox | Software Engineer | Washington, DC + San Francisco, CA + Berlin, Germany + REMOTE | Full-time | https://www.mapbox.com/
At Mapbox, we’re building the future of mapping and location. We build tools for developers to add beautiful and functional maps and location-based services to their websites and applications. We’re obsessed with individual growth and learning, building teams with diverse perspectives, and showing empathy for our teammates and customers.
We’re a fairly distributed team, with large offices in Washington, DC and San Francisco, CA, and satellite offices in Berlin, Helsinki, Bangalore, and Shanghai. There are limited opportunities for remote work on a team-by-team basis. As a result, we prioritize self-directedness and strong written communication skills.
Aside from our mobile SDKs, our codebase is largely nodeJS and React, although we write C++ where performance truly matters, as well as some Python for data wrangling and machine learning. We don’t mind if you haven’t worked with our stack before as long as you’re excited to learn!
I know that we've done this before, but as you might know, visa things can be so case-by-case that it's hard to generalize. I'd encourage you to apply to a position that interests you on our website and mention your question to our recruiting team.
I love using your products! heads up for the awesome mapbox-gl.js. Perfomance is a beast, assuming it's running on web-gl 1 / 2. Plus the simple API's.
I'm not sure I agree that "the company has a huge incentive in firing him a day before he vests." First of all, the direct and indirect costs of hiring a replacement can be massive, and potentially larger than the value of the stock that isn't vested. An employee doesn't have to be "essential" in order to be extremely valuable, especially early on. Second, a company could make a habit of firing employees right before they vest, and even if they managed to completely mitigate the damage internally as soon as word got out about this practice they would suddenly find it impossible to hire quality talent.
Currently, pilots have the primary function of monitoring the automation, and exercising critical thinking when things go wrong. This can be everything from flight computers misbehaving, dealing with weather, avoiding turbulence, troubleshooting an engine fault indicator. Their secondary function of flying the plane is more or less because they have to be there anyway, and for almost all larger aircraft the autopilot takes care of everything except for maybe the first few minutes and the last ~1 to 10 minutes of the flight (although this varies based on airline, aircraft type, and individual pilot preference).
Autoland does exist (google cat. 3 ILS), but it hasn't really been refined to perfection because the pilots aren't going anywhere as of yet and they are quite excellent at performing the last 200 ft of the landing sequence (once the runway is in sight in low vis conditions).
Your average airline flight, in daylight and good weather, can really be quite a simple exercise. The outliers, with equipment out of service, bad weather, icing, or really serious things like an engine failure, are extremely cognitively demanding in a very "human" way and certainly outside the capabilities of perhaps anything short of an artificial general intelligence. I think the future of computing in the cockpit for the foreseeable future involves collaborating with humans to help them navigate these challenging situations and focus on higher level problem solving, rather than replacing them.
I'm guessing fishing story == a huge exaggeration.
As in, when you catch a normal sized fish and then go home and tell your friends about the massive fish you caught and how it put up a fight for two hours.
This is a cool idea, and something I would consider using. Two questions:
- How are you sourcing your translations, and what is your technology solution for matching them up?
- Do you have plans to make a native app for tablets that can store the books locally, or are you going to move ahead with improving your web tool for now?
The new books we're adding come from Project Gutenberg. We parse the ebooks, and using our tool and a combination of open source technologies (http://mokk.bme.hu/en/resources/hunalign/ for instance) we do the matching and then manually review the result, if necessary.
We started building an app, but then realised that the interface would basically be the same, and a lot can be accomplished with modern web technologies. We plan to leverage local storage and to make it feel as native as possible for now, since we have fairly limited resources and a lot of work in front of us.
I don't mean to be discouraging or 'that person', but I'm curious if you're aware of Nuzzel and how you would plan to differentiate from their product.
How do you initially plan to structure your loans/agreements (interest rate, term, etc) - and what adjustments will you consider making from those numbers to acquire users?
Initially we are beginning with quinquennial term lengths with a fairly conservative price structure. However, our model is being designed with the end-state in mind.
In our end-state, we intend to have full flexibility on any lever possible.
For example, say you have the option of covering your education in return for 5% of your income for 20 years. This may be equivalent to 10% over 10 years, or 20% over 5 years (please note these are purely for illustrative purposes and not discounted/run through our pricing model).
You will have the option of having a sliding scale for income sharing %, term, etc. It should look near-continuous rather than discrete. Sliding one parameter will certainly drag the others but we will provide recommendations based on what the user's financial needs are.
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We have an exceptionally data-heavy focus. As for adjustments, this model will take inputs (e.g. GPA) such that when a student does well in class, they can "refinance" to perhaps take off a basis point or two depending on the model output which could be significant over the course of repayment (a la "Get an A in Organic Chemistry and save $120 on average over the course of your repayment period."). This can also apply to when a student acquires an internship or their first job which gives an idea of starting salary. More data will allow us to more accurately predict their future earnings potential. Realistically, these adjustments can apply to anything with a statistically significant impact on a student's future earnings potential.
We believe this will allow for students to feel a sense of control over their education financing.
It seems that the difference lies in the possible upside. Human clones currently don't seem likely to have much to offer. Conscious AI would have the potential to aid in the solution of a multitude of problems.
Cloning has a huge amount to offer. First things that come to mind are cloning highly intelligent or successful people, or growing brain dead clones for medical experimentation and organ transplants.
We've managed to succesfully halt progress on these technologies and even make them unthinkable, just by having a cultural bias against them. There's no reason we couldn't do the same for AI.
You're definitely right on the second point...especially with a few more decades of advances in transplant technology.
Seems like there is a innate human revulsion towards clones that has aided/led to the formation of this cultural bias. Unsure how easy it would be to replicate those feelings towards AI. Perhaps widespread acknowledgement of the potential dangers would be the first step.
Anecdotally, people seem to have trouble grasping the dangers involved. They think of the iPhone in their pocket and can't possibly see the risk. Obviously it's near impossible to put yourself in the shoes of an entity that could be orders of magnitude more intelligent than the most intelligent human.
