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:
30min: Take an untimed coding test and access the application form upon
passing.
45min: Non-coding conversational interview
Chat with Ana (no whiteboarding or quizzing theory/architecture/etc)
2-6 hr: Small untimed take-home project
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.
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.
* _Apply here:<https://careers.monadical.com/> *_