Joining Latent Logic means exposure to the latest state-of-the-art research on deep learning. It means building a route from the lab to the real world, to turn great ideas into products with real impact. It means the flexibility to think and create in ways that work for you, with straight-forward and open communication in a diverse, friendly team. Everyone has a key role to play in shaping our culture and ways of working. We look for people who want to get stuck in and be part of building something special for the long term.
Read about Latent Logic, and what it's like to work here, from our team members.
When did you join Latent Logic, and how did you find out about it?
I joined in September last year. I was working as post-doc in Prof. Zisserman’s Visual Geometry Group at Oxford, when Shimon approached me on getting some advice on the topics of human recognition and tracking. That was when I met Joao [our CTO] and found out about Latent Logic. After my post-doc position with Prof. Zisserman ended, Shimon mentioned that they were looking for someone to lead the Computer Vision side of Morpheus Labs [Latent Logic’s former name], so that was how I ended up in this job.
What’s your role at Latent Logic?
As the head of Computer Vision, my team’s job is to provide the data that the Learning team need for their models to learn from. We build Computer Vision tools to extract information about the behaviour of humans from raw video. What specific things I will be working on always depends on what we’re trying to learn and what the source data is. Now that my team has grown to have two engineers, I do a bit less engineering but am still involved in the prototyping of new methods, determining which methods to use, and making sure the code works as it should.
How do you like the team?
The two guys on my team are very smart, really brilliant, and I really appreciate them. They’ve both settled in quickly and work hard, so it’s great to work with them. In the broader team, I think everyone is intelligent and friendly, which makes this a nice place work.
What is the big challenge you’re facing right now?
3D bounding boxes! To provide the Imitation Learning algorithms with expert data to learn from, we need to get the 3D trajectories of vehicles, and to get accurate 3D trajectories we need each vehicle’s 3D co-ordinates in each frame. And we are doing this using only a monocular camera, which is really hard. Other techniques use binocular cameras or LiDAR, but no good methods have been developed for solving this problem using a single, monocular camera, so we have to develop our own!
What is the most exciting part of the job?
My excitement comes from solving problems. I really like that we always have new and different challenges that we get to work on. It makes the work rewarding and always challenging. This field is so large that we cannot be experts at everything, so we are always learning new things, reading about the state-of-the art, and then trying to improve it.
How long have you been at Latent Logic?
I’ve been here for exactly a month now. Before that I was doing my master’s degree in computer science at the University of Oxford, after which I took a month off before joining Latent Logic. Before the master’s I spent 2 years as a software engineer at Goldman Sachs.
How did you find out about Latent Logic?
Omar, who was doing the same MSc as me and also works here, was doing his MSc thesis with Shimon (our co-founder and Chief Scientist). I knew that he had applied to Latent Logic and had decided to join. He told me what the company is about and what it does, which was how I found out about the company. It sounded really interesting and I was looking for a role that would be close to cutting edge research in deep learning with social impact, which made me apply as well.
In the month you’ve been here, what kind of work have you been doing?
I’m in the Computer Vision team. We’re responsible for extracting information from raw traffic footage. I first had to get up to speed on and how our systems work, and have since been doing a fair amount of engineering. A lot of what I’m doing involves improving the code or adding visualisation features, to better understand what is working well and what isn’t and why. More recently, I’ve been involved with the automatic extraction of statistical information like speed distributions, turning statistics, and traffic flow, all from raw traffic footage, as well as detecting anomalous behaviours. The key is to make the process generalisable, so we can apply it to footage of any traffic scene.
What’s your overall impression of the team, and the company?
It’s very different from working at Goldman Sachs! We’re still a small company, and I really like the fact that I can talk to our CTO or CEO at any time. And even more importantly, I can really see how the work I’m doing affects the company as a whole, how it contributes to solve a particular problem which helps the company achieve its goals.
What have you learned since joining Latent Logic?
Well my programming background was in Java and functional languages, so I’ve improved my Python skills a lot.
I’ve also had to familiarise myself with the models we use for detections, segmentation, and tracking, understanding them and their architecture. We’re now working to improve on them to better suit our needs. Since, I didn’t have a background in computer vision – my previous experience was largely in software engineering and in machine learning applied to music – the algorithms we use for detection and tracking vehicles have been new to me and fun to learn.
We'll occasionally add new team member interviews. Check out the Blog page for interviews with more members of the team!
We are always looking for people who get excited about the same things we do, especially:
We take full-time hires and interns. Prior work experience or knowledge of autonomous cars is not required.
Even if you’re not looking right away or we don’t have the right role for you right now, send us an email and your CV and we can keep in touch.Vacancies