Monadical Careers: Senior Machine Learning Engineer

Work Environment

Company Culture

Monadical is a fully-remote software consultancy that prides ourselves on maintaining a healthy work/life balance and supportive remote working culture. We strive to build a diverse, friendly, and knowledgeable team, and expose ourselves to a variety of industries and learning environments. We work with a wide range of clients on medium/large projects, ranging from 3-12+ months. The industries we’ve worked with are varied, from real-estate to finance to biomedicine and neuroscience. Most of our clients find us via word-of-mouth or by reading our blog posts and watching our employees talks.

We try to build our company structure to ensure employees have lots of freedom to choose the projects they enjoy most, and the power to have a real impact on product decisions and company direction. We also have a strong set of company principles that drive who we work with and how we maintain our culture. The company principles handbook is a collaborative, public effort and is editable by any employee by submitting a pull-request.

Perks

Some perks of working with us include:

• Work from home (we’re fully remote!)
• Flexible working hours
• Six weeks of paid vacation
• Competitive salary
• Time, funding, and support for self-improvement/blogging/talks/side-projects/FOSS contribution
• Strong culture emphasis on individual autonomy and impact on company direction

Job description

We are seeking an experienced Machine Learning Engineer with a strong background in designing, implementing, and optimizing machine learning models. The ideal candidate should have a solid understanding of Large Language Models (LLMs), generative models, and reinforcement learning. Most importantly, we value open and inclusive communication. We are looking for candidates who can communicate fluently with team members and clients to distill product requirements and outline a development path with accurate time estimates.

We seek engineers that demonstrate curiosity and a desire to learn and improve, with strong self-direction and self-motivation. We’re a fully remote company, so you should be comfortable getting things done with little oversight! That being said, we enjoy each other’s company on our shared chat system and calls. We have a regular check-in habit to keep people aware of each other’s work and side projects.

Minimum requirements

• Strong programming skills in Python and proficiency with relevant libraries and frameworks
• Solid knowledge of machine learning principles and algorithms, including deep learning approaches
• Strong statistical analysis skills
• Experience with MLOps, data storage solutions, and cloud platforms
• Strong analytical, problem-solving, and debugging skills across systems
• Available during Eastern working hours (10 am - 5 pm EST, flexible)
• Based in Canada

Nice-to-have

• Experience with web development, Rust, or crypto
• Familiarity with large-scale data processing and distributed computing frameworks
• Strong background in mathematics
• Client and product management experience
• Located in or near Montréal

Application Process

Our application process is easy and transparent:

1. 30min: Take an untimed coding test and access the application form upon passing.

2. 45min: Non-coding conversational interview
   Chat with Ana (no whiteboarding or quizzing theory/architecture/etc)

3. 2-6 hr: Small untimed take-home project

4. 60 min: Pairing interview: Add a feature to take-home project with a lead developer

   • First 10 min: Discuss your take-home project and the current state of their code.
   • Next 10 min: Have you step into the shoes of a product manager and discuss potential features to add to their project. Frame it as if it’s a client project and have you explain your thought process when gathering requirements, prioritizing tickets, delegating, and making time estimates.
   • Last 40-60 min: Pair on adding a feature to the codebase together. This interview is to gauge the experience of working together on a technical task, not to measure raw coding speed. We’re more impressed by people who talk clearly through their thought process and code deliberately, than those who try to add as many features as possible in a short time. Treat the task of adding a feature as if it were an un-timed take-home task, and focus on explaining your decisions, more than sheer lines-of-code output during the interview.
   • We’ve all had interview jitters, so if the feature was not completed during pairing or if you feel the interview wasn’t a good representation of your abilities, you’re welcome to push further commits up to 24 hours afterward with a short description of the changes made, and we’ll include it with equal weight when evaluating the codebase.

5. 60min: Pairing interview: same structure as above, pairing with another lead developer

The whole process usually takes 2-3 weeks depending on the applicant pipeline and response times.