We value curiosity, innovation, and an ambitious mindset.

We are looking for people who are inspired to do impactful ML work, learn from other industry-leading minds, publish at and attend top tier AI conferences, solve challenges as a team and be recognized for success.

Our ideal candidates have a solid track record in industry or academia. We value foundational contributions to research and open source projects, experience with deploying complex ML systems, and exceptional collaboration.

View current openings

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

102 people
21 PhDs
19 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 ignite 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

Software Engineer II – Framework

We are looking for world-class engineers to tackle cutting-edge problems of applying Machine Learning in the real world. Join this team to develop a framework for machine learning models that have real world impact. The goal of the framework is to help our ML Scientists build robust and high performing models through providing a solid foundation and allowing for fast iteration.

Technical Product Owner

You will own the delivery of machine learning solutions & work of up to 5 colleagues. Melding expertise in data science, technology, and product management, you embrace the challenge of solving strategic business problems with innovative, scalable products. You are a natural collaborator and influencer. You resolve complex technical problems and effectively communicate to your audience, both technical & not, to achieve a common vision. You have a passion for building a better solution, every time. You naturally provide your colleagues with the inspiration and tools to execute with speed and impact, becoming true leaders themselves.

Machine Learning Engineer

We are looking for experienced Machine Learning Engineers who have worked under tight deadlines and on challenging tasks. The ideal candidate is a strong coder with solid machine learning engineering experience. They should also have expertise in data engineering, machine learning system design and MLOps.

Research Machine Learning Scientist

Research, develop, and apply new techniques in deep learning to advance our industry leading products. Work with large-scale, real-world datasets that range from banking transactions, to large document collections.

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 a remote or in-person interview at our MaRS offices in downtown Toronto.

02

Technical tests

Your main interviews will be conducted with the appropriate team lead. Based on your desired role you will complete a combination of technical tests in ML, coding, engineering design and mathematics/statistics to assess your relevant problem solving abilities.

03

Final interview

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

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