LLM Engineer
Content + Source + Freshness • 12 Dec 2025 • 95% confidence
Offer value
Great entry-level role for emerging ML engineers looking to gain practical experience in a collaborative environment.
- Entry-level role with practical ML experience opportunities
- Mentorship and support from seasoned engineers
- Flexible working arrangement within Canada
- Focus on foundational ML tasks with growth potential
Pros
- Hands-on experience in machine learning implementation
- Supportive learning environment from experienced engineers
- Flexibility in remote working options across Canada
Cons
- Limited decision-making authority
- Entry-level role may entail basic tasks
- Requires adaptability in a rapidly evolving field
Who it's for
Entry-level • Remote
Good fit
- Aspiring ML engineers with a growth mindset
- Graduates seeking practical experience in ML
- Individuals who enjoy collaborative technical work
Not recommended for
- Advanced professionals looking for leadership roles
- Those preferring research-only positions
- Candidates hesitant to take initiative
Motivation fit
Key skills
About the job
About CoLab
As an LLM Engineer at CoLab, you’ll work closely with our ML team to support model development, deployment, and maintenance. You’ll help with data prep, pipeline updates, and implementation tasks that form the backbone of our AI-powered features.
This is a hands-on, execution-focused role, ideal for someone early in their ML career. You’ll be expected to learn fast, work through ambiguity, and bring care and consistency to the details. Over time, you’ll gain exposure to the full ML lifecycle—from building datasets to deploying production models—and grow your skills alongside experienced engineers.
If you're excited about AI/ML, want to contribute to a real product, and are ready to do the foundational work required to make ML systems run reliably at scale, we’d love to hear from you.
About The Role
You’ve got a basic understanding of ML and NLP concepts, can write python code confidently, and are eager to get your hands dirty. You're looking for a role where you can grow your skills by doing real work—not just reading research papers.
You’re detail-oriented, resourceful, and take pride in getting things right the first time—even if the work is unglamorous. You don’t wait to be told what to do; you take initiative, ask good questions, and always follow through.
This role isn’t for someone looking to focus purely on research or theoretical ML. It’s for someone who wants to learn by building and supporting the systems that make AI work in the real world.
Job Responsibilities
- Support ML model development by preparing datasets, writing scripts, and maintaining training pipelines
- Help implement and test model features, experiments, and infrastructure updates
- Contribute to code that integrates ML models into our production environment
- Assist in building evaluation pipelines and observability dashboards for ensuring and maintaining the quality of our ML models going to production.
- Monitor model behavior in production and help troubleshoot performance issue
- Assist with setting up automated testing, logging, and reporting
- Work with the ML, Architecture, and platform teams to keep infrastructure clean, reproducible, and scalable
- Write documentation and contribute to reusable components that improve team efficiency
- Stay curious—ask questions, share what you learn, and actively look for ways to improve tools or processes
Qualifications
- Bachelor's degree (or close to completing one) in Computer Science, Engineering, Data Science, or a related field
- Familiarity with Python and ML libraries like Scikit-Learn, Hugging Face, or PyTorch
- Exposure to NLP or AI concepts (e.g., transformer architecture, embeddings, LLMs)—even via personal projects or coursework
- General familiarity with LLM optimization techniques, including zero shot, one shot and few shot prompt engineering, RAG, fine tuning, CoT, ReACT, etc.
- Some experience with databases and writing queries (SQL or vector DBs like Pinecone, OpenSearch is a plus)
- Interest in cloud tools (e.g., AWS, GCP, Azure) and ML services in the cloud infrastructure
- Experience with Git or version control workflows
- Comfortable working independently and taking ownership of tasks with guidance
- Strong written and verbal communication skills
- A growth mindset—you’re open to feedback and eager to improve every day.
Extra Details
- Compensation: This is a full-time, permanent position with a competitive compensation package that includes a stock options package
- Benefits: This role offers an extended health and benefits package that includes unlimited paid vacation and RRSP matching
- Remote/Hybrid Work: Our main office is in St. John’s, NL, where we offer hybrid and remote opportunities. This role has the flexibility to work from anywhere within Canada.
Equity Note
Frequently cited statistics show that people who identify with historically marginalized groups are likely to apply to jobs only if they meet 100% of the qualifications. We encourage you to help us break that statistic and apply even if you don’t meet every single qualification—your potential is what matters most to us.
