FlightAware is the world's largest and most sophisticated aviation data platform. We run a network of 30,000+ terrestrial ADS-B receivers, fuse 50+ third-party data feeds from around the world, process 180+ million messages per hour, track 100k+ flights per day from gate-to-gate or hangar-to-hangar, and run thousands of machine learning models in production that the world's largest airlines and airports rely on for daily operational efficiency.
I'm an engineering director at FlightAware running our "Aviation Insight" group, which is responsible for the architecture and reliability of FlightAware’s core flight tracking distributed infrastructure, the quality and accuracy of all flight and surface tracking results, and our end-to-end machine learning and model serving platform for computing live ETAs. We're expanding the group and hiring for a number of roles. If you're interested in distributed systems, machine learning, Rust, Python, Spark, or aviation, I'd love to hear from you. (You may have seen the recent blog post from our group on Prometheus and Grafana: https://news.ycombinator.com/item?id=24126088)
FlightAware is expanding its world-class team that builds the predictive technology behind the FlightAware Foresight product suite. FlightAware Foresight is already used by some of the largest airports and airlines in the world to improve operational efficiencies on the ground and in the air. As a senior data scientist at FlightAware, you will work alongside talented developers, systems engineers, and analysts to design, evaluate and improve state-of-the-art machine learning models underlying both current and future Foresight products.
You would bring scientific rigor and statistical methods into a multi-disciplinary engineering group pushing the industry forward and tackling some of the hardest problems in this space. We train thousands of models on hundreds of thousands of CPU cores using datasets with hundreds of millions of examples, and we stream real-time flight data from around the world through these models running on multiple clusters in order to produce thousands of inferences per second. These models compute estimated runway landing and gate arrival times for commercial and GA flights around the world. FlightAware in general processes over 180 million incoming messages per hour from over 22,000 individual data feeds. You would bring a broad technical and engineering background with you, allowing you to design solutions that can scale and operate in this demanding environment.
We're looking for someone with a solid real-world track record of delivering complex machine learning systems in production that solve well-defined business problems. This is decidedly a senior-level role, although less experienced but truly exceptional candidates will be considered. We have no formal requirements about educational background; while a PhD is probably a plus, it's not required, and we're more impressed by real-world results. You need a nontrivial expertise in statistics, machine learning, statistical learning, and deep learning that goes beyond what a talented engineer might learn studying in the evenings for fun. You also need a solid engineering foundation; you wouldn't be isolated from infrastructure and systems engineers but would be working with and alongside them.
FA is a small company (currently ~100 employees), but we're not a startup. We've been around for over a decade and don't rely on VC funding. Our Foresight team alone produces millions of dollars of revenue. The company is successful, profitable, and growing. And we just built out a brand new modern office space in Houston. To apply, please send me an email using the email in my profile here.
This is an opportunity to have a potentially huge impact on machine learning not just at FlightAware but on the aviation industry in general. There are a lot of fascinating and challenging problems in this area: computing taxi times, landing times, departure times, airport congestion, flight delays, and more based on complex real-time contextual information. I honestly believe these are among the most interesting problems you'll find to work on almost anywhere.
FlightAware has a vast amount of highly granular flight data going back many years to facilitate tackling these problems. For instance, we have detailed surface movement data for all aircraft on the ground at most major worldwide airports. We have detailed weather records and radar imagery. We have thousands of live ADS-B receivers around the world. And we've partnered with Aireon to deploy ADS-B receivers in space on dozens of satellites in orbit; this will allow us to achieve global tracking coverage, even over the oceans and other large bodies of water.
FlightAware wants to be on the forefront of tackling these problems using modern, sophisticated methods. We view this as a long-term strategic initiative for the company.
You'd be the first full-time machine learning engineer, so we're looking for someone fairly senior and experienced. You won't be a cog in the machine. This is not just a research position and will involve building end-to-end production systems, from training pipelines to real-time inference engines, so we're ideally looking for someone with a demonstrated track record of doing so. With that said, we're willing to consider less experienced candidates with exceptional backgrounds.
FA is a small company (currently 70-80 employees), but we're not a startup. We've been around for over a decade and don't rely on VC funding. The company is successful, profitable, and growing. And we just built out a brand new modern office space in Houston.
I'm a lead developer at FlightAware. I'm currently leading our flight tracking team, which develops and maintains a distributed system for tracking flights in parallel, and I'm in the process of building a machine learning team for the first time ever at the company.
This is an opportunity to have a potentially huge impact on machine learning not just at FlightAware but on the aviation industry in general. There are a lot of fascinating and challenging problems in this area: computing taxi times, landing times, departure times, airport congestion, flight delays, and more based on complex real-time contextual information. I honestly believe these are among the most interesting problems you'll find to work on almost anywhere.
FlightAware has a vast amount of highly granular flight data going back many years to facilitate tackling these problems. For instance, we have detailed surface movement data for all aircraft on the ground at most major worldwide airports. We have detailed weather records and radar imagery. We have thousands of live ADS-B receivers around the world. And we've partnered with Aireon to deploy ADS-B receivers in space on dozens of satellites in orbit; this will allow us to achieve global tracking coverage, even over the oceans and other large bodies of water.
FlightAware wants to be on the forefront of tackling these problems using modern, sophisticated methods. We view this as a long-term strategic initiative for the company.
You'd be the first full-time machine learning engineer, so we're looking for someone fairly senior and experienced. You won't be a cog in the machine. This is not just a research position and will involve building end-to-end production systems, from training pipelines to real-time inference engines, so we're ideally looking for someone with a demonstrated track record of doing so. With that said, we're willing to consider less experienced candidates with exceptional backgrounds.
FA is a small company (currently 70-80 employees), but we're not a startup. We've been around for over a decade and don't rely on VC funding at all. The company is successful, profitable, and growing. And we just built out a brand new modern office space in Houston.
I'm a lead developer at FlightAware. I'm currently leading our flight tracking team, which develops and maintains a distributed system for tracking flights in parallel, and I'm in the process of building a machine learning team for the first time ever at the company.
This is an opportunity to have a potentially huge impact on machine learning not just at FlightAware but on the aviation industry in general. There are a lot of fascinating and challenging problems in this area: computing taxi times, landing times, departure times, airport congestion, flight delays, and more based on complex real-time contextual information. I honestly believe these are among the most interesting problems you'll find to work on almost anywhere.
FlightAware has a vast amount of highly granular flight data going back many years to facilitate tackling these problems. For instance, we have detailed surface movement data for all aircraft on the ground at most major worldwide airports. We have detailed weather records and radar imagery. We have thousands of live ADS-B receivers around the world. And we've partnered with Aireon to deploy ADS-B receivers in space on dozens of satellites in orbit; this will allow us to achieve global tracking coverage, even over the oceans and other large bodies of water.
FlightAware wants to be on the forefront of tackling these problems using modern, sophisticated methods. We view this as a long-term strategic initiative for the company.
You'd be the first full-time machine learning engineer, so we're looking for someone fairly senior and experienced. You won't be a cog in the machine. This is not just a research position and will involve building end-to-end production systems, so we're ideally looking for someone with a demonstrated track record of doing so. With that said, we're willing to consider less experienced candidates with exceptional backgrounds.
FA is a small company (currently 70-80 employees), but we're not a startup. We've been around for over a decade and don't rely on VC funding at all. The company is successful, profitable, and growing.
FlightAware is the world's largest and most sophisticated aviation data platform. We run a network of 30,000+ terrestrial ADS-B receivers, fuse 50+ third-party data feeds from around the world, process 180+ million messages per hour, track 100k+ flights per day from gate-to-gate or hangar-to-hangar, and run thousands of machine learning models in production that the world's largest airlines and airports rely on for daily operational efficiency.
I'm an engineering director at FlightAware running our "Aviation Insight" group, which is responsible for the architecture and reliability of FlightAware’s core flight tracking distributed infrastructure, the quality and accuracy of all flight and surface tracking results, and our end-to-end machine learning and model serving platform for computing live ETAs. We're expanding the group and hiring for a number of roles. If you're interested in distributed systems, machine learning, Rust, Python, Spark, or aviation, I'd love to hear from you. (You may have seen the recent blog post from our group on Prometheus and Grafana: https://news.ycombinator.com/item?id=24126088)
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