We value curiosity, innovation, and an ambitious mindset.

Ability to do impactful ML work, learn from other industry-leading minds, contribute to successful outcomes, publish/attend top tier ML conferences, win competitive challenges as a team and get recognized.

Solid track record in academia and industry to untangle the world’s most complex problems. Access to unique datasets and agile team. Short time to production means seeing meaningful impact quickly.

View current openings

Immerse yourself in our culture of learning where you can grow and learn at the cutting edge of ML.

56 people
18 PhDs
18 countries of origin

We aim high and do good!

We focus on doing a few things really, really well and continue raising the bar year after year. We build solutions with broad, positive impact that we can be proud of.

We’re committed to fill your curiosity.

We share a hunger to learn! Everyone is encouraged to try something ambitious and meaningful, and learn from mistakes and successes in equal measure.

We are inclusive and we win together.

We foster a place where everyone feels included, cared for and safe to be who they are. We help each other succeed and win as a team, as Layer 6!

Join us

Sr. Machine Learning Product Engineer

The Machine Learning Product Engineer team at Layer 6 focuses on building industry-leading data-centric systems and model delivery systems. Our solutions include data pipelines, feature data lake, systemic data validation and automation of key activities in model delivery including model validation, shakedown, inference, and maintenance.

Machine Learning Engineer – Full Stack

We are looking for world-class engineers to tackle cutting-edge problems of applying Machine Learning in the real world. Join this team to help develop a system that will serve as the main interface to our machine learning platform. The goal of this system is to help in all stages of model development, from feature engineering all the way to production monitoring.

Machine Learning Engineer – Backend

We are looking for world-class engineers to tackle cutting-edge problems of applying Machine Learning in the real world. Join this team to help develop a system that will serve as the main interface to our machine learning platform. The goal of this system is to help in all stages of model development, from feature engineering all the way to production monitoring.

Machine Learning Scientist

In this role you will research, develop, and apply new techniques in the intersection of deep learning and personalization to further advance our industry leading product. We’re looking for someone with a PhD in Computer Science, Statistics, Operations Research, Mathematics or a related field.

Our team is growing fast

We’re always looking for ambitious, execution-oriented people. Submit your CV to careers@layer6.ai and let’s chat.

Our hiring process

01

Apply

Start by submitting your CV. If your credentials meet the requirements for the role, we’ll schedule an in-person interview at our MaRS offices in downtown Toronto.

02

Technical test

Your first interview will be conducted with the appropriate tech lead. You will complete three stages (Math, Coding and ML) of technical testing to assess your relevant problem solving abilities.

03

Final interview

Following the technical interview, you will meet with one of our founders to discuss your previous work/school experience and your aspirations in machine learning.

04

Receive offer

Only the most technically accomplished candidates with a strong culture fit will be offered employment packages.

Technical test sample questions

Coding Question: Implement a function to compute the number of occurrences of a digit k in all numbers between a and b where a >=0 and b >= a. Estimate the runtime of your function. You can use Java, C++, Python or Scala.

Math Question #1: Write a function sample_x( ) to draw independent samples of a random variable with probability density function

Assume you have a function RNG( ) that returns independent floats uniformly distributed in [0,1]. Can you find a way of doing this that runs in a deterministic amount of time?

Math Question #2: Consider the loss function L(x, y) = ½ ( ax² + by² ). Here x and y are the parameters, and a and b are positive constants. For what values of the learning rate will gradient descent converge to the minimum?

Ready to be a part of something big?

Submit your CV