dfine

This is an instructive article for how to get a lot done to improve systems as a "utility knife" developer in a growing startup, but how to do it responsibly.

Can't help but think that this vindicates Boeing's bet on the smaller 787.

New York NYC | Customer Success Engineer

Dynamic Yield’s unified customer engagement platform helps marketers increase revenue by automatically personalizing each customer interaction across the web, mobile web, mobile apps and email. The company’s advanced customer segmentation engine uses machine learning to build actionable customer segments in real time, enabling marketers to take instant action via personalization, product/content recommendations, automatic optimization & real-time messaging.

As a member of the Customer Success Team, your main objective is to assist with project delivery, maintaining a high level of satisfaction for our customer.

What you need to succeed:

  – You will have a technical degree as well as working experience in a professional services/consulting environment of an enterprise software company or consultancy. Self-taught hackers are also welcome. 
  – Knowledge of online marketing functions is an asset, though not required. You will be comfortable responding to the varying demands of working for a dynamic, international company. 
  – Bonus if you have experience working with online Publishers or eCommerce. 
  – You are a self-motivated individual, with well-developed inter-personal & communication skills and a strong desire to succeed.

  – Fluency in JavaScript skills and experience with jQuery. 
  – HTML / CSS and web technologies across different platforms. 
  – Business analysis and understanding of the digital marketing space, especially in the field of online marketing.

Hi, I work at Placemeter, the company that produced this map and the technology behind it. Check out our privacy principles [0] and feel free to email me [1] if you have any questions. Thanks!

0: http://www.placemeter.com/privacy

1: david [at] placemeter [dot] com

Context: http://www.fastcompany.com/3056581/fast-feed/marc-andreessen...

The one liner is right below the larger headline on the landing page, perhaps we should've made it more prominent: "The Placemeter Sensor measures pedestrian, vehicle, and bicycle traffic in real-time."

The quality of the view and the angle determine a lot, but generally our accuracy is above industry average for human counters and other systems.

Placemeter – New York, NY or Paris, France – Computer Vision Engineers FULL TIME

APPLY HERE: http://grnh.se/1yvxfb

We are looking for computer vision engineers from entry level to experienced level, to extend, develop, and maintain our algorithm stack.

  + You will design the next generation of computer vision algorithms
  + You will optimize and deeply understand these algorithms and scale them
  + You will design and maintain the quality assessment tools required to make sure
  our algorithms perform well

ABOUT PLACEMETER

Placemeter uses computer vision algorithms to create a real time data layer about places, streets, and neighborhoods. Placemeter’s technology gives businesses, cities, and people the ability to take a place’s pulse.

APPLY HERE: http://grnh.se/1yvxfb

Not quite sure I understand your question. We store counts, not individual object IDs, so at an individual granularity it would be the same as your IR device counting one person.

Great questions.

1) the processing is happening aboard the sensor, counts are what are sent back to the servers

2) we don't do unique identification like that. We use object detection, which is different than using unique biometric markers like face detection. That means that we can track a person or a car within a frame of view, but not if they exit and re-enter the frame like in the case you described.

Does Density still use wifi pinging for part of its counts?

For the record, that's not what Placemeter does. We don't individually identify people with our algorithms. (I am a PM at Placemeter.)

I am a PM at Placemeter. We do not "use facial recognition software" in our algorithms, as Density's website claims of video-based systems. None of our algorithms use biometric markers for our counting—we're essentially the same "dumb" counters as Density's IR with the added advantage of accuracy and area of coverage.

Placemeter does much more than pay lip service to privacy. We pride ourself on our privacy efforts. If you want to lear more about them, @afar email me: david@placemeter.com

Placemeter – New York, NY or Paris, France – Computer Vision Engineers FULL TIME

APPLY HERE: http://grnh.se/1yvxfb

We are looking for computer vision engineers from entry level to experienced level, to extend, develop, and maintain our algorithm stack.

  + You will design the next generation of computer vision algorithms
  + You will optimize and deeply understand these algorithms and scale them
  + You will design and maintain the quality assessment tools required to make sure
  our algorithms perform well

ABOUT PLACEMETER

Placemeter uses computer vision algorithms to create a real time data layer about places, streets, and neighborhoods. Placemeter’s technology gives businesses, cities, and people the ability to take a place’s pulse. APPLY HERE: http://grnh.se/1yvxfb

The reason why I think it's iOS-ification rather than an OSX-ification is because "Photos" seems less like a standalone app and more like a necessary utility to manage content created by the Apple ecosystem. iPhoto never really felt like that.

iPhoto was part of the iLife suite that were standalone apps to manage the different multimedia demands of your life, mostly from non-Apple digital cameras. It was optimized around editing and organizing your photos. "Photos" on iOS was a barebones app required by a device where camera functionality is built in, intended primarily to manage photos created by the iOS device. Over time both of the apps have borrowed features from each other and grown together until it made sense to converge into one app.

The iOS-ification of Mac OS continues apace. A good thing, in my opinion.

Placemeter – New York, NY or Paris, France – Computer Vision Engineers FULL TIME

APPLY HERE: http://grnh.se/1yvxfb

We are looking for computer vision engineers from entry level to experienced level, to extend, develop, and maintain our algorithm stack.

  + You will design the next generation of computer vision algorithms
    to extract more from our video feeds.
  + You will use and master the tools to build, 
    optimize and deeply understand these algorithms and scale them.
  + You will design and maintain the quality assessment tools 
    required to make sure our algorithms perform well in all cases

ABOUT PLACEMETER

Placemeter uses computer vision algorithms to create a real time data layer about places, streets, and neighborhoods. Placemeter’s technology gives businesses, cities, and people the ability to take a place’s pulse.

APPLY HERE: http://grnh.se/1yvxfb

Placemeter — NYC or PARIS (relocation possible), FULL-TIME

We're looking for a COMPUTER VISION ENGINEER to join our world class CV team.

Job posting and details here: http://grnh.se/emdq87

About Placemeter

Placemeter is building a real-time data layer measuring activity in the physical urban environment, like how many people are walking on an intersection or how fast cars are speeding down your block. We use computer vision at a massive scale, on a large number of rich and ubiquitous video feeds, to understand what is going in in the physical world in real time. We measure how busy places are, what people do, how fast cars go, and much more. We offer that data to developers, citizens, cities, and retailers, radically changing the way they interact with the physical world.

We built our platform around privacy. We never store any video and we do not identify people. We also make sure no one can reverse engineer our data to identify anyone. We are backed by top NYC & Silicon Valley VCs, alumna of TechStars (Spring 2013), and actively plugged into their vibrant ecosystem of mentors and alumni.

We need creative and flexible minds, with a complete commitment to building nothing else but perfect software and systems. Make a real impact on your city, the NYC tech community, and a fast growing startup. Put your mark on this truly disruptive, slightly crazy, and ambitious platform we are building. Placemeter is in a phase of rapid expansion, and we want you to join us.

APPLY NOW: http://grnh.se/emdq87

About our stack

Our system is full stack in a way rarely seen before, from low level embedded processing to computer vision algorithms to mobile applications, and everything in between including: machine learning, data analytics, prediction models, and geospatial intelligence.

If you want to build the next big thing in machine learning, computer vision, sensing, prediction and if you like huge, scalable and impactful systems, you will fit right in. You will encounter some of the biggest tech challenges you have ever seen. Get ready to earn some serious tech street cred.

We are a paradise for video and data geeks. Using our own optimized code base, we detect moving objects, classify them, then track their positions. We then use trajectory information to estimate speed as well as location occupancy and traffic. Today, our computer vision stack runs continuously on close to 1,000 available video feeds, collecting 8 million data points each day on average. We extract insights and predictions from these points. We have millions of ground truth data points to build and optimize our algorithms. We analyze all these data points by comparing them, normalizing them, correlating them with external factors to give our users clean, real time data. We are about to grow dramatically, adding a couple of orders of magnitude to our current scale.

We work in a data driven environment where every new algorithm is first defined by data sets and ground truth - we have a lot of data floating around. Our regression and quality tests guarantee that each improvement on one camera will improve our quality and performance overall.

We highly value testing and continuous integration. For critical interactions between major components we maintain integration tests, and for our core algorithms we maintain quality and regression tests. Good test coverage is key to keeping our bug count low. It also builds internal confidence to work on any piece of code without fear of breaking existing functionalities.

Our tech team is made up of varied backgrounds, and we function as a flat team where everybody knows about what everyone else is working on. This creates an environment where you can learn from your peers with ease and significantly grow your tech turf.

APPLY NOW: http://grnh.se/emdq87

The best rejoinder to this might be the Founder's Fund manifesto, which originally started with, "We wanted flying cars, instead we got 140 characters."

That specific phrase has since disappeared. But it illustrates that we can enjoy—even invest in—certain innovations like social media, while bemoaning the lack of innovation in other spaces (eg transportation in this example).

Here's the modified version of the manifesto:

http://www.foundersfund.com/the-future/

Placemeter* will turn your old phone into a smart sensor that helps to measure pedestrian traffic and car speed in your neighborhood: http://placemeter.com

*I'm Product Manager there

Thanks!

thanks, tobé!

thanks!

thanks!

thanks, Jeff!

Placemeter — NYC (relocation possible)

Multiple jobs on the tech side: https://jobs.lever.co/placemeter?lever-source=hacker-news

  + Full stack engineer
  + Computer Vision Engineer
  + Data Science Engineer
  + Mobile/Embedded Sensor Engineer
  + Product Manager

We built our platform around privacy. We never store any video and we do not identify people. We also make sure no one can reverse engineer our data to identify anyone. We are backed by top NYC & Silicon Valley VCs, alumna of TechStars (Spring 2013), and actively plugged into their vibrant ecosystem of mentors and alumni.

We need creative and flexible minds, with a complete commitment to building nothing else but perfect software and systems. Make a real impact on your city, the NYC tech community, and a fast growing startup. Put your mark on this truly disruptive, slightly crazy, and ambitious platform we are building.

Placemeter is in a phase of rapid expansion, and we want you to join us.

APPLY NOW: https://jobs.lever.co/placemeter?lever-source=hacker-news

Computer Vision Engineer — Placemeter — NYC, REMOTE, OR VISA

About Placemeter

Placemeter uses public video feeds and computer vision algorithms to create a real time data layer about places, streets, and neighborhoods. Check out our algorithm in action here: http://placemeter.com/tech

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Our system is full stack in a way rarely seen before, from GPU processing to mobile application, and everything in between including computer vision and data analytics models and equations. If you like to learn about really kick ass technology in AI, data science and sensing and modeling, if you like huge, scalable systems designed to make a real impact, you will fit right in, and you will be faced with some of the best tech challenges you have seen so far. Get ready to seriously increase your tech street cred.

We are a paradise for video and data geeks. You will have the opportunity to experiment with a completely unique data set that measures the “busyness” of the real world in real time, and for every place out there! For that, we ingest and process video and data feeds at a scale never heard of. And this is growing every day.

We are looking for our lead computer vision engineer. In this position, you will be in charge of designing the next generation of computer vision algorithms or data analytics and prediction models we use to quantify the world. You will define the methodologies and set the standards of algorithmic development.

Placemeter will never identify people. Placemeter’s technology and cameras will never be used to individually track people.

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SKILLS YOU WILL NEED OR DEVELOP

  + Computer vision – 
     -SIFT/SURF
     -Haar cascades
     -scale-space models
     -object tracking
     -kalman filtering
     -any other video analytics or pattern matching approach
  + C/C++
  + Code optimization, multithreading, parallel architectures
  + Software development methodologies
  + Continuous integration, test driven development, agile methodologies
  + Algorithmic quality measurement and testing

  + A hands-on programmer with experience deploying at scale
  + Multiple languages: 
     -you can prototype in matlab or F
     -then code in java, python, or C++
     -and you can look into lower level layers for optimization
  + Data and ground truth driven algorithm development

  + A strong need to know what you’re doing in the long term, to view the big picture
  + Respect and practical use of theoretical knowledge
  + A love for beautiful code
  + A real, pragmatic approach to your every day work
  + A team player
  + Humility, respect, and a tremendous desire to learn

APPLY NOW

If you're interested in working with us, send us an email and we'll chat!

Email address: jobs@placemeter.com

Subject line: HN Computer Vision Post

[Edits: concision & formatting]

I don't necessarily love paywalls but it seems a bit uncalled for to automate an already well known workaround. Should we be actively subverting business models?

Placemeter — NYC/REMOTE — FREELANCE — UI/UX

Hiring now!

We're looking for a UI/UX designer to optimize our onboarding flow. Should be able to design mobile-first, responsive and work with developers on comps. This should be a quick project, but we like to work with known entities and there will be more work down the road.

To Apply

Email: jobs@placemeter.com

Subject: Designer from HN

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About Placemeter

Placemeter uses public video feeds and computer vision algorithms to create a real time data layer about places, streets, and neighborhoods. Check out our algorithm in action here: http://placemeter.com/tech

Contains the best author disclaimer I've seen:

Data analysis by Eugene Bialczak. Also, a disclaimer: the author wrote much of the IMDb Trivia App.

This book features two adverbs on the cover page.