Machine Learning Researcher

About you:

You're a brilliant computer scientist or mathematician. Not only do you keep up-to-date with the latest breakthroughs in machine learning, but you strive to make those breakthroughs yourself. You have a deep understanding of the theory and mathematics that underpin modern machine learning, and you've developed a strong intuition for how to apply and improve these methods in practice. You love data which challenges your expectations, and you're excited to dive into the world of biosignals and wearable technology.

What you’ll do:

  • Within 2 Weeks, you have:
    • Met with the entire Machine Intelligence team and have a good understanding of what everyone is working on
    • Understood the background on all of the team’s projects, how we got here, and where you fit into the team
    • Reviewed everything we currently understand about the project you’ll be working on, and started a literature review to further develop your understanding
    • Set up your ML framework of preference and/or the framework we are currently using for your project
    • Formulated a plan for the dataset required (depending on the state of the project), or begin to dive deep into the data we have and understand the intricacies hidden within it
  • Within 1 Month, you have:
    • Begun contributing to our team brainstorming sessions, providing us with new ideas and avenues to explore
    • Established a solid grasp of your project and have already implemented initial models which demonstrate the feasibility of solving this challenging problem
    • Collaborated with other teams in software, hardware, and/or advanced research and development to push the capability of what Focals can do
  • Beyond this, you have:
    • Taken on and started to lead more projects, sometimes dealing with multiple projects simultaneously
    • Demonstrated your independence by continuing to innovate - designing, developing, and evaluating models that can achieve the seemingly impossible
    • Continued researching and improving upon the state-of-the-art in machine learning
    • Worked with a variety of data from several domains, including biosignals, adding new and unique features to the product via a broad understanding of the user’s context and situation
    • Dealt with variations in user behaviour and anatomy by exploring, comprehending, and masterfully integrating the challenging edge cases

What you need:

  • Strong theoretical knowledge, including mathematical foundations, of modern machine learning systems and techniques
  • Practical experience implementing and expanding on a variety of machine learning models
  • Strong statistical background and ability
  • Experience working with both real-time data and large offline datasets
  • Excellent coding skill in Python or similar
  • Advanced degree (Masters or PhD) in computer science, engineering, or equivalent, with a focus on machine learning
  • Excellent written and verbal communication skills

Feel like you can’t tick all the boxes above? If you have some of the skills and experience that we’re looking for and are willing to use your talent to learn the rest, we encourage you to apply!

Why North?

Day-to-day, we challenge each other to constantly raise the bar, encourage unconventional thinking to achieve innovative breakthroughs, and are passionately committed to surpassing our goals. We advocate a healthy lifestyle and promote continuous learning in a flexible work environment. Most of all, we set visionary goals, and we’re passionate about building the best, most impactful products that people will love.

About Us

North, formerly Thalmic Labs, builds products that change the way we see and engage with our world. North’s latest product, Focals, are custom-built glasses with a display that only the wearer can see. Focals let you see texts, get turn-by-turn directions, check the weather, request an Uber, ask Alexa, and more — seamlessly and immediately. Founded in 2012, North has grown to a world-leading team of engineers, researchers, designers, and creators committed to building a future where technology is there when you need it and gone when you don’t, hidden by design.