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.

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

38 people
15 PhDs
18 countries of origin

We care about our employees.

We offer generous packages of pension plans, health coverage, visa support and vacation. We have healthy and low-sugar snacks in the office.

We’re committed to fill your curiosity.

We provide unique opportunities for our team to learn from the researchers at Vector Institute. We encourage everyone to challenge themselves and acquire knowledge at international competitions and conferences. We organize lunch & learn series to share our findings and learn from each other.

We have a driven, diverse, and amiable culture.

We are self-motivated, entrepreneurial, competitive yet humble and nice to each other. We are open minded and provide everyone with equal opportunities to achieve their professional goals. We work extremely hard but still take time to have fun and exciting activities!

Join us

Sr. Full Stack Engineer

We are looking for someone armed with a strong tool-kit to develop and maintain technical solutions that adhere to engineering and architectural design principles while meeting business requirements. You’ll also provide technical expertise with a focus on efficiency, reliability, scalability, and security includes planning, evaluating, recommending, designing, operationalizing, and supporting solutions in compliance with enterprise and industry standards. At Layer 6, you’ll be inspired both by the work we do and the people who make it all happen.

Data Engineer, Machine Learning

Robust, trustworthy, and efficient data system is crucial for developing and deploying ML models in production. In addition to handling the complexity of massive data sources, ML data system also needs to provide strong support for data science specific tasks.

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